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This papers proposes an refinement of the taxonomy of mobile apps in tourism roposed by Kennedy-Eden and Gretzel (2012) from two perspectives: a taxonomy on what services travel-related apps provide to the user and a taxonomy based on the functionalities. The taxonomies provide insights into app development trends as well as gaps in the mobile app landscape. A few emblematic case studies from European cities are analysed. Managerial and policy making implications are provided.

It is our great pleasure to welcome you to the Fourth IEEE International Workshop on Emerging COgnitive Radio Applications and aLgorithms (CORAL 2016), held in Coimbra, Portugal on June 21, 2016, in conjunction with the IEEE WoWMoM 2016 Conference.

Background:There is consensus that dementia is the most burdensome disease for modern societies. Few cost-of-illness studies examined the complexity of Alzheimer's disease (AD) burden, considering at the same time health and social care, cash allowances, informal care, and out-of-pocket expenditure by families.Methods:This is a comprehensive cost-of-illness study based on the baseline data from a randomized controlled trial (UP-TECH) enrolling 438 patients with moderate AD and their primary caregiver living in the community.Results:The societal burden of AD, composed of public, patient, and informal care costs, was about €20,000/yr. Out of this, the cost borne by the public sector was €4,534/yr. The main driver of public cost was the national cash-for-care allowance (€2,324/yr), followed by drug prescriptions (€1,402/yr). Out-of-pocket expenditure predominantly concerned the cost of private care workers. The value of informal care peaked at €13,590/yr. Socioeconomic factors do not influence AD public cost, but do affect the level of out-of-pocket expenditure.Conclusion:The burden of AD reflects the structure of Italian welfare. The families predominantly manage AD patients. The public expenditure is mostly for drugs and cash-for-care benefits. From a State perspective in the short term, the advantage of these care arrangements is clear, compared to the cost of residential care. However, if caregivers are not adequately supported, savings may be soon offset by higher risk of caregiver morbidity and mortality produced by high burden and stress. The study has been registered on the website www.clinicaltrials.org (Trial Registration number: NCT01700556).

The deployment of Structural Health Monitoring (SHM) systems is a natively interdisciplinary task that involves joint research contributions from sensing technologies, data science and civil engineering. The capability to assess, also from remote stations, the working conditions of industrial plants or the structural integrity of civil buildings is widely requested in many application fields. The technological development aims to continuously provide innovative tools and approaches to satisfy these demands. As a first instance, reliable monitoring strategies are needed to detect structural damages while filtering out environmental noise. Ongoing solutions to tackle these topics are based on the exploitation of highly customized sensing technologies, such as shaped transducers for Acoustic Emission (AE) testing or Micro-Electro-Mechanical System (MEMS) accelerometers for Operational Modal Analysis (OMA) [1]. On the other hand, effective data acquisition and storage techniques must be employed to cope with the heterogeneity of the sensing devices and with the amount of data produced by collecting raw measured signals. Finally, damage detection and prediction tasks should be computed via data-driven algorithms that can complement the model-based alternatives traditionally used in civil engineering. Layered SHM architectures [2] represent straightforward approaches to address the system complexity originated by this interdisciplinary design; however, few real-world implementations have been presented so far in the literature. In this paper, we overcome these limitations by presenting an Internet of Things (IoT)-based SHM architecture for the predictive maintenance of industrial sites and civil engineering structures and infrastructures. The proposed cyber-physical system includes a monitoring layer, that consists of accelerometer-based sensor networks, a data acquisition layer, built on the recent W3C Web of Things standard [3], and a data storage and analytics layer, which leverages distributed database and Machine Learning tools. We extensively discuss the hardware/software components of the proposed SHM architecture, by stressing its advantages in terms of device versatility, data scalability and interoperability support. Finally, the effectiveness of the system is validated on a real-world use-case, i.e., the monitoring of a metallic frame structure located at the SHM research labs of the University of Bologna, Italy, within the MAC4PRO project [4].

The Web of Things (WoT) standard recently promoted by the W3C constitutes a promising approach to devise interoperable IoT systems able to cope with the heterogeneity of software platforms and devices. The WoT architecture envisages interconnected IoT scenarios characterized by a multitude of Web Things (WTs) that interact according to well-defined software interfaces; at the same time, it assumes static allocations of WTs to hosting devices, and it does not cope with the intrinsic dynamicity of IoT environments in terms of time-varying network and computational loads. In this paper, we extend the WoT paradigm for cloud-edge continuum deployments, hence supporting dynamic orchestration and mobility of WTs among the available computational resources. Differently from state-of-art Mobile Edge Computing (MEC) approaches, we heavily exploit the W3C WoT, and specifically its capability to standardize the software interfaces of the WTs, in order to propose the concept of a Migratable WoT (M-WoT), in which WTs are seamlessly allocated to hosts according to their dynamic interactions. Three main contributions are proposed in this paper. First, we describe the architecture of the M-WoT framework, by focusing on the stateful migration of WTs and on the management of the WT handoff process. Second, we rigorously formulate the WT allocation as a multi-objective optimization problem, and propose a graph-based heuristic. Third, we describe a container-based implementation of M-WoT and a twofold evaluation, through which we assess the performance of the proposed migration policy in a distributed edge computing setup and in a real-world IoT monitoring scenario.

In the aftermath of a large-scale emergency, Unmanned Aerial Vehicles (UAVs) can play a key role as mobile communication systems supporting rescue operations on the ground. At the same time, the deployment of autonomous UAV swarms still poses severe challenges in terms of distributed mobility, swarm connectivity and mesh networking. To this purpose, we propose mathsf {ELAPSE} (aErial LocAl Positioning System for Emergency), a novel, distributed framework for aerial mesh deployment that supports discovery and multi-hop connectivity among rescue personnel and emergency requesters. mathsf {ELAPSE} integrates components of swarm mobility, positioning and Quality-of-Service (QoS) support, while targeting UAV devices at different levels of hardware complexity. Three contributions are provided in this study. First, we present a novel, bio-inspired swarm mobility algorithm which natively addresses QoS-based aerial mesh connectivity, coverage of the ground nodes and UAV collision avoidance through the abstraction of virtual springs. Second, we investigates its implementation when geo-location capabilities are not available: to this aim, we propose local-based and cooperative-based techniques through which each UAV can estimate the position of its neighbours, and hence correctly adjust its direction and speed. Third, we analyze the feasibility of the mathsf {ELAPSE} framework through a twofold evaluation: i.e. a large-scale OMNeT++ simulation showing the effectiveness of the distributed mesh formation and localization techniques, and a small-case ground robotic testbed demonstrating the impact of QoS mechanisms on the system operations.

Bluetooth Mesh is a recent SIG standard enabling the deployment of multi-hop Wireless Sensor Networks (WSNs) over Bluetooth Low Energy (BLE) communication links. The standard introduces many novel and interesting features in the Internet of Things (IoT) domain, such as the seamless integration among sensors and mobile and wearable devices, and the support for a wide range of different IoT application profiles. At the same time, fine-grained assessments of the performance are still needed to understand the potential of the technology. In this paper, we investigate the usage of Bluetooth Mesh solutions for the joint monitoring of indoor spaces and humans. Through the deployment of a test-bed, we evaluate the performance of Bluetooth Mesh WSNs under varying traffic loads and network sizes. In addition, by exploiting the short-range, multi-hop communications, we propose a procedure for the indoor localization of mobile devices and evaluate its accuracy. The results demonstrate that the technology supports reasonable delivery ratio under high traffic loads, however the network and localization performance sharply decreases when increasing the number of hops between the source and destination nodes.

Digital Twins are crucial in Industry 4.0 IoT scenarios, as they replicate physical assets and enable important tasks such as predictive analytics, what-if scenarios and real time monitoring. The heterogeneity of IoT use cases usually makes the development of digital twins extremely application-specific as well as prone to interoperability issues. To overcome these two challenges, we propose WoTwins, a framework that, on one side, leverages the W3C Web of Things (WoT) standard to model data and entities, and, on the other side, generates automatically Digital Twins of existing Web Things by modeling their state space through a Markov Decision Process (MDP) graph and by predicting its behavior though Machine Learning techniques. We conduct experiments on a simulated use cases related to IoT robotics to evaluate our proposal

The integration of legacy IoT ecosystems in Industry 4.0 scenarios requires human effort to adapt single devices. This process would highly benefit from features like device lookup, loose coupling and late binding. In this paper, we tackle the issue of integrating legacy monitoring systems and actuation systems in an industrial scenario, by looking into the Web of Things (WoT) as a communication standard and the Eclipse Arrowhead Framework (AHF) as a service orchestrator. More specifically, we propose a general architectural approach to enable closed-loop automation between the above mentioned legacy systems by leveraging the adaptation of the WoT to the AHF. Then, we develop a rule-based engine that enables the control of the actuation based on sensor values. Finally, we present a proof-of-concept use case where we integrate a Structural Health Monitoring (SHM) scenario with a power control actuation subsystem using the developed components

Recent Structural Health Monitoring (SHM) systems might take advantage of Internet of Things (IoT) technologies for fine-grained and autonomic sensors data management and processing. Moreover, current SHM deployments often demand for the installation of multi-type and heterogeneous sensor devices capable to perform long-term measurements; from here, the need for dedicated software platforms allowing for scalability and interoperability requirements arises. In this paper, we jointly address the two issues above by proposing MODRON, which is a SHM-dedicated IoT platform with sensor-to-cloud support. The software architecture leverages the W3C Web of Things (WoT) standard for multi-source sensors data acquisition and fusion. The platform includes an edge component, implementing the communication with the monitoring layer and the data exposition through WoT Web Things (WTs), and a cloud component, embedding sensor/WT management capabilities, which is in charge of distributed data storage, aggregation, visualization and analytics. We illustrate the abstract MODRON architecture and its current implementation that supports two different SHM sensor types (MEMS accelerometers and piezoelectric devices). In addition, we describe the system operations on a real-world SHM system, i.e. the monitoring of a metallic structure instrumented with multiple sensor networks.

The ability of unmanned aerial vehicles (UAVs) to communicate and cooperate among themselves in an aerial swarm network is of paramount importance for collaborative missions. It enables the swarm to function as a collective unit to plan trajectories, share tasks, coordinate flight route for collision avoidance or to optimize a given mission objective. In this work, a cellular radio interface known as "sidelink"is investigated for establishing efficient UAV-to-UAV (U2U) multi-hop communication within the aerial swarm network. First, we look into the salient features of cellular sidelink (PC5 radio interface) as a key enabler of U2U communication, and then present a multi-hop network model design to extend cellular connectivity services to out-of-coverage UAVs during missions. Using extensive simulations, we evaluate and study the performance of sidelink-assisted multi-hop U2U communication model in terms of the packet delivery ratio (PDR) for different key scheduling parameters of mode-4 sensing-based autonomous resource selection.

The chaotic growth of the Internet of Things (IoT) determined a fragmented landscape with a huge number of devices, technologies and platforms available on the market, and consequential issues of interoperability on many system deployments. The recent W3C Web of Things (WoT) standards aimed to ease the deployment of heterogeneous systems by introducing uniform and well-defined software interfaces among the systems’ components. Although the WoT reference architecture is generic and agnostic to the target devices, its widespread adoption depends on the availability of specific tools named Servients, which enable the run-time operations of WoT applications. In this paper we aim at contributing to the adoption of the W3C WoT standards by presenting WoT Micro-Servient (WMS), a framework for bringing the WoT paradigm to the extreme edge of an IoT environment. Through WMS, developers can design, compile and install WoT applications on micro-controllers and embedded systems with constrained hardware capabilities. We describe the architecture and functionalities of the tool, and demonstrate its effectiveness in terms of reduced latency and energy consumption compared to the state-of-art proxy-based solution enabled by Node-wot, i.e. the official implementation of W3C WoT. Finally, we discuss a real-world application related to smart home, where WMS is used to enable a WoT-based remote monitoring and control of indoor plants, by enabling seamless integration between micro-controllers and mobile devices.

We propose a framework called AirID that identifies friendly/authorized UAVs using RF signals emitted by radios mounted on them through a technique called as RF fingerprinting. Our main contribution is a method of intentionally inserting 'signatures' in the transmitted I/Q samples from each UAV, which are detected through a deep convolutional neural network (CNN) at the physical layer, without affecting the ongoing UAV data communication process. Specifically, AirID addresses the challenge of how to overcome the channel-induced perturbations in the transmitted signal that lowers identification accuracy. AirID is implemented using Ettus B200mini Software Defined Radios (SDRs) that serve as both static ground UAV identifiers, as well as mounted on DJI Matrice M100 UAVs to perform the identification collaboratively as an aerial swarm. AirID tackles the well-known problem of low RF fingerprinting accuracy in 'train on one day test on another day' conditions as the aerial environment is constantly changing. Results reveal 98% identification accuracy for authorized UAVs, while maintaining a stable communication BER of 10 -4 for the evaluated cases.

Warehouse management is a crucial task for most of nowadays' business activities. The usage of small Unmanned Aerial Vehicles (UAVs) has been recently proposed to automatize the inventory process while increasing the safety for human workers. However, the practical deployment of UAV swarms in the target use-case must face many severe technical issues, such as the indoor navigation, the package identification and the limited flight autonomy of the drones. In this challenging context, the paper addresses three novel research contributions. First, we propose a generic architecture for UAV-based inventory management within large-scale warehouses, including the components of UAV path planning, package identification (via QR Codes), data validation (via the Blockchain) and wireless charging; a prototype implementation of the data acquisition and management framework has been conducted by using low-cost mini-drones and single-board computers. Second, we analyze the system performance and specifically the trade-off between the inventory accuracy, i.e. rate of successful package identification, and the inventory completion time. Third, we derive the optimal UAV mobility parameters in terms of speed and number of visits for each shelf unit, and test the system operations and the configuration parameters through a small-case testbed.

The IoT is spreading heavily around us and many new standard at different architectural layers proliferate. This creates a heavy interoperability gap that many architectural proposals and new standards have tried to cope with over the years. In particular, we take into account the Arrowhead interoperability Framework, a powerful and promising paradigm for interconnecting local clouds and we demonstrate the possibility to integrate it with the emerging W3C Web of Things standard. We study the usage of the Arrowhead Framework as a potential candidate for discovery operation and inclusion of WoT ecosystems within Service Oriented Architectures (SOA), in order to support the integration of legacy and proprietary systems. To this aim, a three-layered architecture is proposed and validated experimentally on a proof-of-concept use case, by highlighting the advantages of the proposed integration.

Smartphone devices can play a key role on emergency scenarios thanks to their pervasiveness, and the possibility to convey emergency requests from the involved people to the rescue teams. At the same time, the effective utilization of such devices on critical scenarios with limited mobile Internet access is challenging. As an alternative, several recent research studies have proposed Emergency Communication System (ECS) based on short-range Device-to-Device (D2D) solutions available on Commercial Off The Shelf (COTS) devices (e.g. Wi-Fi Direct); however, the target of these solutions is constituted by small indoor areas, since the scalability on large-scale environments is often a problem. In this paper, we overcome such issue by proposing LOCATE , a novel phone-based ECS enabling long-range communication among survivors and rescue teams over critical environments where 3/4G cellular connectivity is not available and the traditional geo-localization technologies (e.g. the GPS) provide only partial coverage of the environment. The proposed system consists of a mobile application connected to a LoRa transceiver via Bluetooth Low Energy (BLE); through the app, users can send emergency requests that are re-broadcasted by other peers until reaching a rescue personnel who is able to handle the emergency. Three novel contributions are provided in this paper. First, we provide extensive measurements of the LoRa technology, and investigate its suitability for ECS-related applications. Second, we describe the LOCATE prototype and the enabling algorithms; specifically, we propose a novel multi-hop dissemination algorithm which maximizes the probability to deliver an emergency request to the destination (e.g. rescue personnel) within a user-defined temporal threshold, while minimizing the number of message re-transmissions. Third, we extensively evaluate the LOCATE performance through OMNeT++ simulations, assessing the capability of the dissemination protocol to spread out the emergency requests over large-scale scenarios, and through experiments, assessing the capability of LoRa-based trilateration technique to provide accurate GPS-free localization. The results demonstrate that the LOCATE protocol is able to minimize the time required to handle the emergency when compared to other dissemination strategies (e.g. flooding, continuous, once-per-contact), and they highlight the considerable improvement provided by the LoRa technology over other D2D solutions available on COTS smartphones (e.g. Wi-Fi Direct).

UAV-aided Wireless Sensor Networks (WSNs) constitute an energy- and cost-effective solution for the deployment of large-scale IoT monitoring systems. At the same time, the integration of UAVs into IoT scenarios poses some practical challenges which are far to be addressed, like the synchronization issue (how can the Wireless Ground Sensors (WGSs) be aware of the presence of the UAV?) and the channel contention in high-dense scenarios (how can the WGSs cope with the limited transmission opportunity?). In this paper, we address both the issues above by proposing a novel Energy Load Aware Sensor To aIr Communication (ELASTIC) platform, supporting the deployment of scalable and energy-efficient UAV-aided WSNs. The proposal includes two main components, working in synergy at the PHY and MAC layers. First, the paper describes the design and implementation of Active Wake-up Radio mechanisms through which the WGSs can be notified of the UAV’s presence and hence of the transmission opportunity; the proposed architecture supports two operative modes (Switch and Low-Power) that can be properly orchestrated so that the energy consumption of the WGSs in standby phase is minimized. Second, since multiple WGSs can be activated from a wake-up signal and access the channel at the same time, a novel priority-based MAC scheme has been designed: the proposed solution performs distributed contention control among the WGSs, by taking into account the length of the transmission opportunity for each WGS and the actual channel load. The simulation results demonstrate that the proposed ELASTIC MAC protocol guarantees a +50% throughput improvement when compared to the legacy CSMA/CA MAC in high-dense WSNs, and show its ability to adapt to different traffic loads and UAV speeds.

Unmanned aerial systems (UASs) allow easy deployment, three-dimensional maneuverability and high reconfigurability, as they sustain communication network in the absence of pre-installed infrastructure. The proposed FOg Computing in UAS Software-defined mesh network (FOCUS) paradigm aims to realize an implementable network design that considers practical issues of aerial connectivity and computation. It allocates UASs to the tasks of data forwarding and in-network fog computing while maximizing number of ground-users in UAS coverage. FOCUS improves efficient utilization of network resources by introducing on-board computation and innovates on top of software-defined networking stack by integrating the capabilities of network and ground controllers to enable simultaneous orchestration of both UASs and communication flows. There are three main contributions of the paper: First, a SDN-based architecture is designed enabling autonomous configuration of computation and communication as well as managing multi-hop aerial links. Second, a global optimization problem to achieve optimal forwarding and computational allocation is formulated using Open Jackson Network model and solved via a heuristic approach with well defined complexity. Third, FOCUS framework is implemented on a small-scale testbed of Intel® Aero UASs performing image analysis with a full software stack. Experiments reveal at least 32% latency improvement in computation service time compared to traditional centralized computation at the end-server or greedy task allocation schemes within the network.

Aerial surveillance systems are a cost-effective and on-demand solution for smart city monitoring. However, the deployment of a swarm of unmanned aerial vehicles (UAVs) for continuous video capture of mobile ground target (MGT) activities poses new research issues in terms of energy management, scenario coverage, and multidevice task coordination. In this article, we address these challenges by proposing Persistent Crowd Tracking Using UAVs (PERCEIVE), a novel framework for continuous video surveillance via UAVs that are periodically replenished through mobile charging stations (MCSs).

This paper explores how the national tourism organizations (NTOs) of the top 10 most-visited countries by international tourists strategically employ Facebook to promote and market their destinations. Based on big data retrieved from the NTOs’ Facebook pages, and leveraging advanced metrics for capturing user engagement, the study sheds light on the factors contributing to superior level of social activity. The findings indicate that the way Facebook is tactically employed varies significantly across the NTOs. The panel data regression analyses suggest that engagement is positively affected by posting visual content (namely photos), and posting during the weekends, and negatively affected by evening posting. Post frequency displays no statistically significant effect on social engagement. The study also shows that most of the NTOs (except for Italy, Spain, Turkey and the UK) deploy Facebook with a top-down approach, and spontaneous user-generated content (UGC) is allowed to a very little extent.

Several recent studies demonstrated that the utilization of Unmanned Aerial Vehicles (UAVs) in combination with Wireless Sensor Networks (WSNs) can enhance the system performance in terms of lifetime and data delivery ratio; at the same time, the communication among Wireless Ground Sensors (WGSs) and UAVs poses several technical challenges which are far from being solved. Specifically, in this paper, two main issues are addressed: (i) how to ensure seamless synchronization among ground and aerial devices at each transit, and (ii) how to compute energy-efficient schedules for the WGSs and feasible trajectories for the UAVs. To this purpose, BEE-DRONES, a novel framework for data collection in wake-up radio based UAV-aided WSNs is proposed; in order to solve the synchronization issue, a solution where UAVs transfer energy toward selected WGSs is presented. The solution allows WGSs to power down the main radio when not requested. The joint WGSs wake-up scheduling and UAV path planning optimization problem is formulated, by taking into account the limited autonomy of the UAVs, the energy consumption of both UAVs and WGSs, and the data requirements of the applications, and it is solved via a two-step heuristic. The OMNeT++ simulation results demonstrate that the BEE-DRONES framework is able to enhance the WSN lifetime, and to optimize the quality of gathered data in terms of minimal temporal/spatial correlation.

Nowadays, Unmanned Aerial Vehicles (UAVs) represent a significant aid on scenarios where fixed, ground infrastructures are temporarily or permanently not available; this is the case of large-scale applications of the Internet of Things (IoTs), e.g. smart city and agriculture 3.0, where the UAVs can be employed as mobile data mules and gather the data from Wireless Ground Sensors (WGSs). UAV-aided wireless sensor networks (WSNs) introduce considerable advantages both in terms of performance and costs since they avoid the need of error-prone multi-hop communications, and also the installation of static gateways; at the same time, they pose formidable research challenges for their implementation, like the synchronization issue between the UAV and the WGS and the path planning, which should take into account the extremely limited flight autonomy of the UAVs. In this paper, we address both the issues above by proposing BEE-DRONES , a novel framework for large-scale, ultra low-power UAV-aided WSNs. In order to mitigate the synchronization problem, we investigate the utilization of passive Wake-up Radio (WR) technology on the WGSs, and of wireless power transfer from the UAVs: by harvesting the energy from the UAV hovering over it, the WGS is activated only for the short time required to transfer the data toward the mobile sink, while it experiences zero-consumption in sleep mode. We investigate the performance of passive WR-based WGS through real measurements, under different WGS-UAV distances and antenna orientations. Then, based on such results, we formulate the joint WGS scheduling and UAV path planning problem, where the goal is to determine the optimal trajectory of the UAVs activating the WR-based WGSs while taking into account the Value of the Sensing (VoS) as well as the total lifetime of the WSN. The original problem is transformed into a multi-commodity flow problem, and both centralized and distributed heuristics over the multi-graph are proposed. Finally, we evaluate the proposed algorithms through extensive OMNeT++ simulations; the results demonstrate the gain of BEE-DRONES in terms of extended lifetime compared to traditional, non WR-based solutions (e.g. duty-cycle), and in terms of reduced data-correlation compared to non VoS-aware path planning solutions.

In a VANET communication, link stability can neither be guaranteed nor make\ud the established route link permanent due to the dynamic nature of the network. In V2V communication\ud without the involvement of any infrastructural units like RSU access points or gateway,\ud the probability of successful link establishment decreases when vehicle’s speed varies, red traffic\ud light increases, cross-road increases and finally when the density of the running vehicles is sparse.\ud To ensure route establishment and control route request broadcast in a sparse VANET with crossroad\ud layout, RSUs are used in this paper for route discovery within one gateway zone when a\ud next hop vehicle to relay the route request packet is unavailable. RSUs are static but the vehicles\ud are dynamic in nature, so relying completely on RSU for forwarding data is not recommended\ud because chances of link failure, link re-establishment, and handoff overhead will be high. So, in\ud this paper, RSUs and Gateways are evoked for route discovery and data forwarding only when\ud necessary. Moreover, a local route repair is attempted in this paper when the path length is high\ud to reduce or avoid loss of buffered packets along the route and to maintain a more stable link\ud with the help of RSUs.

Modern smartphones integrate multiple functionalities into a single device: they can establish peer-to-peer wireless links, they can sense the environment through several embedded sensors, they are provided with a multi-core CPU. Hence, they can play a crucial role on emergency scenarios, where there is need of acquiring data from the environment, processing, and quickly conveying them to the people involved. In this paper, we describe how to turn a group of dense and heterogeneous smartphones into a phone-sensing platform, through the SENSE-ME software. This latter integrates three layers: (i) a network layer, that allows the creation of a spontaneous device-to-device communication structure; (ii) a sensing layer, that allows retrieving local and remote sensing data; (iii) a decision layer, that allows mining the sensed data, in order to extract context-aware information and -based on them-infer decisions about proper actions to take. In the following, we describe the SENSE-ME architecture and some enabling technologies at each layer. Moreover, we discuss the open challenges that must be addressed for the complete system deployment.