Internet of Things applications require timely access to information collected from sensors deployed over large geographic areas. However, such applications often experience highly-varying network conditions that prevent the timely delivery of information updates related to source data from sensors. Moreover, IoT applications have different metrics of interest and patterns to request source data. This article explicitly addresses the timely delivery of information updates in heterogeneous IoT scenarios with different application-specific goals. For this purpose, it introduces new metrics based on age of information (AoI) to accurately describe timeliness of updates in such a context. Moreover, it analytically derives optimal update generation policies for different request patterns to minimize the overall update age in an IoT system and maximize fairness of updates. Finally, it carries out a thorough performance evaluation of the proposed policies for representative request patterns with a real-world dataset of Internet connectivity. The obtained results demonstrate that the proposed policies are competitive with those in the state of the art, with a two order of magnitude reduction in energy consumption and up to a$\mathbf{1 9. 9 \%}$higher fairness.

Seamless transition between public dialogue and private talks is essential in everyday conversations. Social Virtual Reality (VR) has revolutionized interpersonal communication by creating a sense of closeness over distance through virtual avatars. However, existing social VR platforms are not successful in providing safety and supporting private conversations, thereby hindering self-disclosure and limiting the potential for meaningful experiences. We approach this problem by exploring the factors affecting private conversations in social VR applications, including the usability of different interaction methods and the awareness with respect to the virtual world. We conduct both expert interviews and a controlled experiment with a social VR prototype we realized. We then leverage the outcomes of the two studies to establish a design space that considers diverse dimensions (including privacy levels, social awareness, and modalities), laying the groundwork for more intuitive and meaningful experiences of private conversation in social VR.

Registration data have several important applications, for instance, in web security and Internet economics. For this reason, they have been made publicly available since the early days of the Internet through the WHOIS lookup service. However, WHOIS provides human-readable textual data without a strict data model, thereby hindering machine interpretation. This work provides a thorough analysis of the Registration Data Access Protocol (RDAP), the successor of WHOIS, with particular focus on its suitability for modern cloud-based applications requiring machine-friendly data and short response times. Accordingly, an in-depth analysis is carried out over a dataset of more than 360k RDAP records as for both the content of the responses and the performance of the protocol in comparison with WHOIS. The analysis reveals that RDAP records are complex to parse, redundant, and subject to response times with high variance. These findings are leveraged to provide recommendations on the future evolution of RDAP to satisfy the requirements of modern applications.

Internet failures occur, resulting in service disruptions as well as monetary losses. Internet measurement platforms run network diagnostic tests to probe and identify anomalies at a global scale, such as slowdowns which can affect quality of service and user experience. However, maintaining such platforms is complex, as it may involve managing large amounts of hardware and servers in addition to non-negligible monetary costs. To address these challenges this article presents CLAIM, a cloud-based framework for Internet-scale measurements. CLAIM supports running custom probes according to a cloud-native design built on top of the serverless computing paradigm and also supports spot virtual machines. CLAIM was implemented and evaluated in different scenarios. The related analysis showed that CLAIM reduces measurement costs up to 90% compared to standard virtual machines, without a noticeable overhead in running probes.

Accurate characterization of Head Mounted Display (HMD) pose in a virtual scene is essential for rendering immersive graphics in Extended Reality (XR). Remote rendering employs servers in the cloud or at the edge of the network to overcome the computational limitations of either standalone or tethered HMDs. Unfortunately, it increases the latency experienced by the user; for this reason, predicting HMD pose in advance is highly beneficial, as long as it achieves high accuracy. This work provides a thorough characterization of solutions that forecast HMD pose in remotely-rendered virtual reality (VR) by considering six degrees of freedom. Specifically, it provides an extensive evaluation of pose representations, forecasting methods, machine learning models, and the use of multiple modalities along with joint and separate training. In particular, a novel three-point representation of pose is introduced together with a data fusion scheme for long-term short-term memory (LSTM) neural networks. Our findings show that machine learning models benefit from using multiple modalities, even though simple statistical models perform surprisingly well. Moreover, joint training is comparable to separate training with carefully chosen pose representation and data fusion strategies.

Modern cloud-based applications are composed of several microservices that interact over a network. They are complex distributed systems, to the point that developers may not even be aware of how microservices connect to each other and to the Internet. As a consequence, the security of these applications can be greatly compromised. This work explicitly targets this context by providing a methodology to assess microservice connectivity, a software tool that implements it, and findings from analyzing real cloud applications. Specifically, it introduces Kubesonde, a cloud-native software that instruments live applications running on a Kubernetes cluster to analyze microservice connectivity, with minimal impact on performance. An assessment of microservices in 200 popular cloud applications with Kubesonde revealed significant issues in terms of network isolation: more than 60% of them had discrepancies between their declared and actual connectivity, and none restricted outbound connections towards the Internet. Our analysis shows that Kubesonde offers valuable insights on the connectivity between microservices, beyond what is possible with existing tools.

Today’s deep neural networks (DNNs) are very accurate when trained on a large amount of data. However, suitable input might not be available or may require extensive data collection. Data sharing is one option to address these issues, but it is generally impractical because of privacy concerns or due to the problematic process of finding a sharing agreement. Instead, this work considers knowledge sharing by first exchanging the weights of pretrained DNNs and then applying transfer learning (TL). Specifically, it addresses the economics of knowledge sharing in AI services by taking a market-based approach. In detail, a model based on Fisher’s market is devised for optimal knowledge sharing, defined as the gain in inference accuracy from exchanging DNN weights. The proposed approach is shown to reach a market equilibrium and to satisfy important economic properties, including Pareto optimality. A technique for weight fusion is also introduced to merge acquired knowledge with the existing one. Finally, an extensive evaluation is conducted in a distributed intelligence scenario. The obtained results show that the proposed solution is efficient and that weight fusion with TL significantly increases inference accuracy compared to the original DNN, without the overhead of federated learning.

Inference carried out on pretrained deep neural networks (DNNs) is particularly effective as it does not require retraining and entails no loss in accuracy. Unfortunately, resource-constrained devices such as those in the Internet of Things may need to offload the related computation to more powerful servers, particularly, at the network edge. However, edge servers have limited resources compared to those in the cloud; therefore, inference offloading generally requires dividing the original DNN into different pieces that are then assigned to multiple edge servers. Related approaches in the state-of-the-art either make strong assumptions on the system model or fail to provide strict performance guarantees. This article specifically addresses these limitations by applying distributed assignment to DNN inference at the edge. In particular, it devises a detailed model of DNN-based inference, suitable for realistic scenarios involving edge computing. Optimal inference offloading with load balancing is also defined as a multiple assignment problem that maximizes proportional fairness. Moreover, a distributed algorithm for DNN inference offloading is introduced to solve such a problem in polynomial time with strong optimality guarantees. Finally, extensive simulations employing different data sets and DNN architectures establish that the proposed solution significantly improves upon the state-of-the-art in terms of inference time (1.14 to 2.62 times faster), load balance (with Jain’s fairness index of 0.9), and convergence (one order of magnitude less iterations).

Long range (LoRa) is one of the most popular technologies for low-power wide area networks. It offers long-range communication with a low energy consumption, which makes it ideal for many applications in the Internet of Things. The performance of LoRa networks depends on the communication parameters used by individual nodes. Several works have proposed different solutions, typically running on a central network server, to select these parameters. However, existing approaches have not addressed the need to (re-)assign parameters when channel conditions suddenly vary due to additional traffic, changes in the weather or the presence of obstacles. Moreover, allocation strategies that require a central entity to decide communication parameters do not scale due to the large number of configuration packets that must be sent to the nodes. To address these issues, this article proposesNoReL, a distributed game-theoretic approach that allows nodes to autonomously update their parameters and maximize their packet delivery ratio.NoReLis based on a stochastic variant of no-regret learning, which is proven to reach an$\epsilon$-coarse correlated equilibrium in LoRa networks. Extensive simulations show thatNoReLachieves a higher delivery ratio than the state of the art in both static and dynamic environments, with an improvement up to 12%.

Future wireless networks should meet heterogeneous service requirements of diverse applications, including interactive multimedia, augmented reality, and autonomous driving. The fog radio access network (Fog-RAN) is a novel architecture that enables efficient and flexible allocation of network resources to end users. However, guaranteeing application-specific service requirements while maximizing resource utilization is an open challenge in Fog-RANs. This article proposes a multiresource Fog-RAN slicing scheme that maximizes network resource utilization and satisfies important economic properties: Pareto-optimality, envy freeness, and sharing incentive. The proposed solution considers both heterogeneous resources (i.e., bandwidth, storage, and computing) and the different service levels defined in 5G networks. Accordingly, a two-level resource scheduling mechanism is devised to jointly allocate Fog-RAN resources to slices in two stages: 1) a broker allocates resources to slices at fog nodes over a given time window and 2) a slice hypervisor then allocates slice-specific resources at each fog node to users with a much shorter time scale. An extensive evaluation based on real-world data sets demonstrates that the proposed solution significantly increases the monetary gain of service providers, namely, by 32%–60% compared to the state of the art, including dynamic hierarchical resource allocation and dynamic slicing with proportional allocation.

Long Range (LoRa) is a low-power wireless communication technology for long-range connectivity, extensively used in the Internet of Things. Several works in the literature have analytically characterized the performance of LoRa networks, with particular focus on scalability and reliability. However, most of the related models are limited, as they cannot account for factors that occur in practice, or make strong assumptions on how devices are deployed in the network. This article proposes an analytical model that describes the delivery ratio in a LoRa network with device-level granularity. Specifically, it considers the impact of several key factors that affect real deployments, including multiple gateways and channel variation. Therefore, the proposed model can effectively evaluate the delivery ratio in realistic network topologies, without any restrictions on device deployment or configuration. It also accurately characterizes the delivery ratio of each device in a network, as demonstrated by extensive simulations in a wide variety of conditions, including diverse networks in terms of node deployment and link-level parameter settings. The proposed model provides a level of detail that is not available in the state of the art, and it matches the simulation results within an error of a few percentage points.

Urban environments are the most prevalent application scenario for the Internet of Things (IoT). In this context, effective data collection and forwarding to a cloud (or edge) server are particularly important. This work leverages opportunistic data collection based on the mobile crowd sourcing (MCS) paradigm for time-sensitive IoT applications. Specifically, it introduces an incentive mechanism for the crowd to collect data that are valuable to data consumers in terms of regions of interest and time constraints. The proposed approach successfully incorporates the willingness of the crowd to participate in the data collection as part of the related incentives. It also ensures collection of valuable data via selective user incentivization. Accordingly, a weighted social welfare maximization problem is defined for users to decide which sensors to visit subject to deadline constraints. Following the NP-hardness of the problem, an online heuristic algorithm is proposed for sensors to dynamically incentivize mobile users with a low message and time complexity. The proposed solution is shown to be effective for time-sensitive quality data collection through extensive simulations on realistic mobility traces. It significantly increases the overall social welfare as well as the amount of collected data compared to other approaches.

The energy consumption of mobile networks is already substantial nowadays, and only expected to further increase with the roll-out of 5G. Base stations are the key elements in this context: reducing their energy consumption is of paramount importance for network operators, not only to lower operating costs, but also to meet sustainable development goals. Today's base stations are typically over-provisioned, i.e., they comprise multiple cells to meet the peak load in a region. Therefore, substantial energy savings are possible by switching off cells that are under-utilized. This article proposes a data-driven approach to determine the time periods when a cell can be switched off. Forecasting is used to accurately predict network utilization and automatically find the time intervals to reliably switch off a cell. We carefully analyze the requirements of the system as a whole, from data collection to forecasting methods, to enable effective energy savings in practice. Considering several real-world traces from LTE networks, we show that an average of 10.24% energy savings is possible. We explore the trade-offs between energy savings and overhead in switching off cells, and provide insights into the choice of methods accordingly. In particular, we show that the accuracy of forecasting is not the most important factor in achieving energy savings; instead, the prediction (uncertainty) interval plays a key role in being able to achieve energy savings with less impact on end-users. Finally, we propose a model to generate utilization traces that match the distribution of real-world traces obtained from cellular networks.

Deep neural networks (DNN) are the de-facto solution behind many intelligent applications of today, ranging from machine translation to autonomous driving. DNNs are accurate but resource-intensive, especially for embedded devices such as mobile phones and smart objects in the Internet of Things. To overcome the related resource constraints, DNN inference is generally offloaded to the edge or to the cloud. This is accomplished by partitioning the DNN and distributing computations at the two different ends. However, most of existing solutions simply split the DNN into two parts, one running locally or at the edge, and the other one in the cloud. In contrast, this article proposes a technique to divide a DNN in multiple partitions that can be processed locally by end devices or offloaded to one or multiple powerful nodes, such as in fog networks. The proposed scheme includes both an adaptive DNN partitioning scheme and a distributed algorithm to offload computations based on a matching game approach. Results obtained by using a self-driving car dataset and several DNN benchmarks show that the proposed solution significantly reduces the total latency for DNN inference compared to other distributed approaches and is 2.6 to 4.2 times faster than the state of the art.

Mobile applications are very challenging to test as they usually have a complex graphical user interface and advanced functionality that involves interacting with remote services. Due to these features, student assessment in courses about mobile application development usually relies on assignments or projects that are manually checked by teaching assistants for grading. This approach clearly does not scale to large classrooms, especially for online courses. This article presents a novel system for automated assessment of Android exercises with cloud-native technologies. Different from the state of the art, the proposed solution leverages a mobile app testing framework that is largely used in the industry instead of custom libraries. Furthermore, the devised system employs software containers and scales with the availability of resources in a data center, which is essential for massive open online courses. The system design and implementation is detailed, together with the results from a deployment within a master-level course with 120 students. The received feedback demonstrates that the proposed solution was effective, as it provided insightful feedback and supported independent learning of mobile application development.

This article introduces the novel concept of distance-dependent barcodes, which provide users with different data based on their scanning distance. These barcodes employ color blending as the key technique to achieve distance-dependence. A simple yet robust encoding scheme is devised accordingly to distinguish between near and far users. Through several experimental results, the proposed technique is shown to be effective (in terms of clear separation between near and far scanners), reliable (as to successful scans), and practical (it can be used in off-the-shelf smartphones). A few representative use cases are then presented to establish distance-dependent barcodes as an enabling technology for context-aware mobile applications. They include casual interactions with public displays, where the role of users is determined based on their distance from a screen, and augmented reality in retail, where distance-dependent barcodes provide information on available goods with different granularities. Finally, distance-dependent barcodes are shown to be user-friendly and effective through a user study.

Camera-display communication leverages visible light to transfer data wirelessly by using a screen as a transmitter and a camera as a receiver. Such an approach faces several challenges to be employed in practice, including unreliable decoding due to imperfect synchronization and channel impairments. This article introduces MAMBA, a mobile application framework for adaptive camera-display communication with color barcodes. MAMBA employs efficient computer vision techniques and scales with the number of blocks in the barcode, rather than with the number of pixels in the captured image. As a consequence, it allows to take full advantage from the high-resolution cameras available on modern mobile devices. In addition, MAMBA realizes an adaptive and bi-directional protocol for camera-display communication with fast feedback. Specifically, MAMBA carries out dynamic adaptation of both frame rate and length based on environmental conditions and the processing capabilities of the devices. Experimental results show that MAMBA is effective, thereby allowing reliable realtime communication in a variety of operating conditions.

Cross-Technology Communication (CTC) allows direct message exchange between devices with different (i.e., incompatible) wireless communication standards. CTC is particularly suitable to allow for coordination between heterogeneous devices sharing the same spectrum, as in the Internet of Things. Existing research on CTC has focused on enabling communications for diverse technologies with the goal of achieving a high throughput. However, it did not address how to establish a link suitable for CTC, which is necessary for successful data exchange. This article specifically addresses such a problem by introducing CTC-CEM (CTC Channel Establishment with Multiple nodes), a scheme to establish a CTC channel involving the use of multiple nodes in a network. CTC-CEM employs duty-cycling and leverages network density to reduce energy consumption, while keeping a low discovery latency. In particular, CTC-CEM defines different discovery protocols to reliably detect co-located networks. Moreover, it addresses the selection of multiple CTC nodes as a set cover problem, and includes an optimization technique based on dynamic programming to balance the energy consumption in the whole network. Extensive simulations show that CTC-CEM effectively distributes the energy consumption in the network, increasing fairness by 97% after optimization. Furthermore, the latency in establishing a channel with CTC-CEM is two orders of magnitude lower than that for device discovery in duty-cycled networks.

Long range (LoRa) is a wireless communication standard specifically targeted for resource-constrained Internet of Things (IoT) devices. LoRa is a promising solution for smart city applications as it can provide long-range connectivity with a low energy consumption. The number of LoRa-based networks is growing due to its operation in the unlicensed radio bands and the ease of network deployments. However, the scalability of such networks suffers as the number of deployed devices increases. In particular, the network performance drops due to increased contention and interference in the unlicensed LoRa radio bands. This results in an increased number of dropped messages and, therefore, unreliable network communications. Nevertheless, network performance can be improved by appropriately configuring the radio parameters of each node. To this end, in this article we formulate integer linear programming models to configure LoRa nodes with the optimal parameters that allow all devices to reliably send data with a low energy consumption. We evaluate the performance of our solutions through extensive network simulations considering different types of realistic deployments. We find that our solution consistently achieves a higher delivery ratio (up to 8% higher) than the state of the art with minimal energy consumption. Moreover, the higher delivery ratio is achieved by a large percentage of nodes in each network, thereby resulting in a fair allocation of radio resources. Finally, the optimal network configurations are obtained within a short time, usually much faster than the state of the art. Thus, our solution can be readily used by network operators to determine optimal configurations for their IoT deployments, resulting in improved network reliability.

The adoption of the Internet of Things is gradually increasing. However, there remains a significant obstacle that hinders its adoption as a truly ubiquitous technology: the ability of constrained devices to unambiguously exchange data with shared meaning. In this respect, the World Wide Web Consortium has developed the Web of Things architecture to provide semantic data exchange. However, such an architecture does not cover all possible use cases and still has important limitations. This article specifically addresses these issues. In particular, it discusses the design and implementation of a solution that extends the Web of Things architecture to achieve a higher level of semantic interoperability for the Internet of Things. The proposed solution relies on a human-assisted translation process and defines an architecture that enhances the semantic compatibility between components in the World Wide Web Consortium and the Internet Engineering Task Force. The effectiveness of the proposed solution is demonstrated through both a quantitative and a qualitative evaluation, in terms of performance and key properties in comparison with the state of the art.

Virtual machine consolidation aims at reducing the number of active physical servers in a data center so as to decrease the total power consumption. In this context, most of the existing solutions rely on aggressive virtual machine migration, thus resulting in unnecessary overhead and energy wastage. Besides, virtual machine consolidation should take into account multiple resource types at the same time, since CPU is not the only critical resource in cloud data centers. In fact, also memory and network bandwidth can become a bottleneck, possibly causing violations in the service level agreement. This article presents a virtual machine consolidation algorithm with multiple usage prediction (VMCUP-M) to improve the energy efficiency of cloud data centers. In this context, multiple usage refers to both resource types and the horizon employed to predict future utilization. Our algorithm is executed during the virtual machine consolidation process to estimate the long-term utilization of multiple resource types based on the local history of the considered servers. The joint use of current and predicted resource utilization allows for a reliable characterization of overloaded and underloaded servers, thereby reducing both the load and the power consumption after consolidation. We evaluate our solution through simulations on both synthetic and real-world workloads. The obtained results show that consolidation with multiple usage prediction reduces the number of migrations and the power consumption of the servers while complying with the service level agreement.

Content providers (CPs) are keen to cache their popular contents in small-cell base stations (SBSs) provided by mobile network operators (MNOs). In fact, they can serve the requests of their subscribers with low latency, thereby increasing user satisfaction. Employing advanced video encoding techniques, such as scalable video coding (SVC), improves the utilization of wireless resources and the network infrastructure. However, the cache trading policies for SVC videos in multi-provider networks have not been studied yet. In this article, we design a commercial trading system in which multiple CPs, each owning SVC videos, compete over renting the cache in multiple SBSs provided by an MNO. We model cache trading between the MNO and CPs as a social welfare maximization problem, whose objective is to maximize the trading profit while achieving the economic properties of rationality, balanced budget, and truthfulness. Since optimal allocation of random-size caches to multiple CPs is NP-hard, we devise an iterative trading mechanism based on double auction called DOCAT, wherein the cache of SBSs is segmented and traded in multiple rounds. In each round of the auction, the MNO and CPs price the cache segments based on their profit, then submit their asking and buying bids, respectively. Next, a many-to-one matching algorithm is run to efficiently find perfect matches between the cache segments and winning CPs. Numerical results based on a real video dataset show that DOCAT increases the social welfare of the system while satisfying the desired economic properties.

Urban environments are a particularly important application scenario for the Internet of Things (IoT). These environments are usually dense and dynamic; in contrast, IoT devices are resource-constrained, thus making reliable data collection and scalable coordination a challenge. This work leverages the fog networking paradigm to devise a multi-tier data offloading protocol suitable for diverse data-centric applications in urban IoT scenarios. Specifically, it takes advantage of heterogeneity in the network so that sensors can collaboratively offload data to each other or to mobile gateways. Second, it evaluates the performance of this offloading process through the amount of data successfully reported to the cloud. In detail, it provides an analytical characterization of data drop-off rates as a random process and derives a light-weight yet efficient method for collaborative data offloading. Finally, it shows that the proposed fog-based solution significantly decreases the data drop-off rate through both analysis and extensive trace-driven simulations based on human mobility data from real urban settings.

Disaster scenarios are particularly devastating in urban environments, which are generally very densely populated. Disasters not only endanger the life of people, but also affect the existing communication infrastructure. In fact, such an infrastructure could be completely destroyed or damaged; even when it continues working, it suffers from high access demand to its resources within a short period of time, thereby compromising the efficiency of rescue operations. This work leverages the ubiquitous presence of wireless devices (e.g., smartphones) in urban scenarios to assist search and rescue activities following a disaster. It considers multi-interface wireless devices and drones to collect emergency messages in areas affected by natural disasters. Specifically, it proposes a collaborative data collection protocol that organizes wireless devices in multiple tiers by targeting a fair energy consumption in the whole network, thereby extending the network lifetime. Moreover, it introduces a scheme to control the path of drones so as to collect data in a short time. Simulation results in realistic settings show that the proposed solution balances the energy consumption in the network by means of efficient drone routes, thereby effectively assisting search and rescue operations.

The article addresses a key challenge in the design of content for pervasive displays: how to engage passers-by who have limited time and attention? To achieve this, we apply a novel approach for computational design of interesting display content using tiled layouts. We present a model of display foraging based on information foraging theory to describe the behavior of a rational but time-limited user looking at a display. Accordingly, our work aims to maximize the information gain for tiled displays. This complex problem is divided into two phases: (1) generating designs of tiled layouts and (2) assigning content options to individual tiles based on what predicted by display foraging. Accordingly, a proof-of-concept system was realized then evaluated computationally and empirically with a control study and field study. The results show that the proposed system can engage significantly more people than typical digital signage.

Recent advancements in virtualization and software architecture have led to the new paradigm of serverless computing, which allows developers to deploy applications as stateless functions without worrying about the underlying infrastructure. Accordingly, a serverless platform handles the lifecycle, execution and scaling of the actual functions; these need to run only when invoked or triggered by an event. Thus, the major benefits of serverless computing are low operational concerns and efficient resource management and utilization. Serverless computing is currently offered by several public cloud service providers. However, there are certain limitations on the public cloud platforms, such as vendor lock-in and restrictions on the computation of the functions. Open source serverless frameworks are a promising solution to avoid these limitations and bring the power of serverless computing to on-premise deployments. However, these frameworks have not been evaluated before. Thus, we carry out a comprehensive feature comparison of popular open source serverless computing frameworks. We then evaluate the performance of selected frameworks: Fission, Kubeless and OpenFaaS. Specifically, we characterize the response time and ratio of successfully received responses under different loads and provide insights into the design choices of each framework.

Caching popular content at small-cell base stations (SCBSs) and user equipments (UEs) can significantly reduce the network backhaul traffic while improving user satisfaction. This is also enabled by novel video encoding techniques, such as scalable video coding (SVC), which combine layers to offer content with different qualities without re-encoding. Despite some recent works, the performance of layered video delivery in crowd-sourced heterogeneous networks (HetNets) is still unexplored. This article provides an analytical characterization the delay of video delivery in a network with multiple cache-enabled SCBSs and UEs, each storing part of the available video layers based on their popularity. Accordingly, video requests from an UE can be served by either SCBSs or UEs nearby. Our main objective is to maximize the cache hit probability by caching appropriate video layers, thereby minimizing the average video delivery delay. We formulate the problem of minimizing the delivery delay of layered video caching as an integer linear program. We then apply the difference of convex functions technique to identify the set of optimal video layers to be cached at each SCBS and UE in an iterative manner. Our results obtained by using a real video dataset demonstrate that our proposed solution significantly reduces the video download time of all UEs in the network.

Vehicular applications in smart cities, including assisted and autonomous driving, require complex data processing and low-latency communication. An effective approach to address these demands is to leverage the edge computing paradigm, wherein processing and storage resources are placed at access points of the vehicular network, i.e., at roadside units (RSUs). Deploying edge computing devices for vehicular applications in urban scenarios presents two major challenges. First, it is difficult to ensure continuous wireless connectivity between vehicles and RSUs, especially in dense urban areas with many buildings. Second, edge computing devices have limited processing resources compared to the cloud, thereby requiring careful network planning to meet the computational and latency requirements of vehicular applications. This article specifically addresses these challenges. In particular, it targets efficient deployment of edge computing devices in an urban scenario, subject to application- specific quality of service constraints. To this end, this article introduces a mixed integer linear programming formulation to minimize the deployment cost of edge devices by jointly satisfying a target level of network coverage and computational demand. The proposed approach is able to accurately model complex urban environments with many buildings and a large number of vehicles. Furthermore, this article presents a simple yet effective heuristic to deploy edge computing devices based on the knowledge of road traffic in the target deployment area. The devised methods are evaluated by extensive simulations with data from the city of Dublin. The obtained results show that the proposed solutions can effectively guarantee a target application- specific quality of service in realistic conditions.

Large-scale Internet of Things (IoT) deployments demand long-range wireless communications, especially in urban and metropolitan areas. LoRa is one of the most promising technologies in this context due to its simplicity and flexibility. Indeed, deploying LoRa networks in dense IoT scenarios must achieve two main goals: efficient communications among a large number of devices and resilience against dynamic channel conditions due to demanding environmental settings (e.g., the presence of many buildings). This work investigates adaptive mechanisms to configure the communication parameters of LoRa networks in dense IoT scenarios. To this end, we develop FLoRa, an open-source framework for end-to-end LoRa simulations in OMNeT++. We then implement and evaluate the Adaptive Data Rate (ADR) mechanism built into LoRa to dynamically manage link parameters for scalable and efficient network operations. Extensive simulations show that ADR is effective in increasing the network delivery ratio under stable channel conditions, while keeping the energy consumption low. Our results also show that the performance of ADR is severely affected by a highly-varying wireless channel. We thereby propose an improved version of the original ADR mechanism to cope with variable channel conditions. Our proposed solution significantly increases both the reliability and the energy efficiency of communications over a noisy channel, almost irrespective of the network size. Finally, we show that the delivery ratio of very dense networks can be further improved by using a network-aware approach, wherein the link parameters are configured based on the global knowledge of the network.

The amount of data generated by sensors, actuators, and other devices in the Internet of Things (IoT) has substantially increased in the last few years. IoT data are currently processed in the cloud, mostly through computing resources located in distant data centers. As a consequence, network bandwidth and communication latency become serious bottlenecks. This paper advocates edge computing for emerging IoT applications that leverage sensor streams to augment interactive applications. First, we classify and survey current edge computing architectures and platforms, then describe key IoT application scenarios that benefit from edge computing. Second, we carry out an experimental evaluation of edge computing and its enabling technologies in a selected use case represented by mobile gaming. To this end, we consider a resource-intensive 3-D application as a paradigmatic example and evaluate the response delay in different deployment scenarios. Our experimental results show that edge computing is necessary to meet the latency requirements of applications involving virtual and augmented reality. We conclude by discussing what can be achieved with current edge computing platforms and how emerging technologies will impact on the deployment of future IoT applications.

With growing data demand and the current dearth of spectrum, mobile operators are looking for new frequency bands to satisfy data-hungry users. One promising avenue of expansion is TV white spaces, which are currently available to secondary users as long as they do not interfere with primary (i.e., incumbent) users. In this work, we explore the benefits of offloading cellular traffic onto TV white spaces. We develop an analytical model and efficient algorithms to assign users to the cellular network or white space channels by considering their channel gains, multi-user interference on white space channels, and the cost of switching between different networks. We perform extensive data-driven simulations in two representative urban scenarios based on publicly available datasets. Our results show that white spaces can increase capacity by 16-62%, depending on the environment, but careful network selection is necessary to ensure that maximum capacity gains are realized. Moreover, we show that white spaces provide a significant benefit in serving indoor users where cellular channel conditions are poor. Specifically, our algorithms can offload up to 40% of cellular traffic to white spaces for indoor scenarios.

This paper addresses the problem of scheduling a single resource to handle requests for time-sensitive periodic services (i.e., data streams) jointly realizing a distributed application. We specifically consider the case, where the demand of each data stream is expressed as a weight relative to a network-wide cyclic schedule. Within this context, we consider the problem of minimizing the schedule length while satisfying the perfect periodicity constraints: the service intervals for the same data stream are fixed and each data stream is cyclically served exactly as many times as its demand. This problem is challenging, as serving a data stream in one time slot might enforce serving it at some specific time slots in the future. As a result, most of the existing solutions have relaxed either the periodicity or the demand constraints of the data streams. In contrast, we study the strict enforcement of both requirements through perfectly periodic schedules. We show that the considered problem is NP-hard and address special cases for which optimal schedules can be derived. We further discuss the more generic instance of the problem represented by an arbitrary number of data streams and demands. Specifically, we provide an approximation algorithm and an efficient greedy solution for such a general case of arbitrary weights. We conduct extensive simulations to evaluate the performance of the proposed solutions. Finally, we show that it is possible to relax the input demands to improve the communication performance at the cost of some other overhead (e.g., in terms of energy consumption).

Robustness in wireless sensor networks (WSNs) is a critical factor that largely depends on their network topology and on how devices can react to disruptions, including node and link failures. This article presents a novel solution to obtain robust WSNs by exploiting principles of biological robustness at nanoscale. Specifically, we consider Gene Regulatory Networks (GRNs) as a model for the interaction between genes in living organisms. GRNs have evolved over millions of years to provide robustness against adverse factors in cells and their environment. Based on this observation, we apply a method to build robust WSNs, called bio-inspired WSNs, by establishing a correspondence between the topology of GRNs and that of already-deployed WSNs. Through simulation in realistic conditions, we demonstrate that bio-inspired WSNs are more reliable than existing solutions for the design of robust WSNs. We also show that communications in bio-inspired WSNs have lower latency as well as lower energy consumption than the state of the art.

Wireless technologies such as WiFi and Bluetooth are widely used for mobile data communications. However, these technologies are affected by interference resulting from shared access to unlicensed frequency bands. Visible light communication is an alternative means for data exchange over an optical channel. It has several advantages over currently used radio-based technologies, including a full-duplex channel and limited interference from other sources. We designed and implemented a system for bidirectional visible light communications between smartphones. Specifically, our solution employs dynamically changing Quick Response (QR) codes shown on the display of one mobile phone to encode the data and the front-facing camera of another phone to receive the data. We devised a supporting communication protocol for reliable communications and realized a file exchange application. Moreover, we carried out an experimental evaluation of our system with focus on communication performance and power consumption. The obtained results show that our solution is reliable and comparable with existing radio-based schemes from the user perspective.

Virtual machine consolidation aims at reducing the number of active physical servers in a data center, with the goal to reduce the total power consumption. In this context, most of the existing solutions rely on aggressive virtual machine migration, thus resulting in unnecessary overhead and energy wastage. This article presents a virtual machine consolidation algorithm with usage prediction (VMCUP) for improving the energy efficiency of cloud data centers. Our algorithm is executed during the virtual machine consolidation process to estimate the short-term future CPU utilization based on the local history of the considered servers. The joint use of current and predicted CPU utilization metrics allows a reliable characterization of overloaded and under loaded servers, thereby reducing both the load and the power consumption after consolidation. We evaluate our proposed solution through simulations on real workloads from the Planet Lab and the Google Cluster Data datasets. In comparison with the state of the art, the obtained results show that consolidation with usage prediction reduces the total migrations and the power consumption of the servers while complying with the service level agreement.

Gene Regulatory Networks (GRNs) represent the interactions of genes in living organisms, which have evolved over millions of years to provide a near-optimal structure for rapid adaptation to the environment. On the other hand, robustness in wireless sensor networks (WSNs) is a critical factor that largely depends on their topology and how quickly the network can recover from node and link failures. This article proposes a novel approach to design robust WSNs by exploiting GRNs. Specifically, we build bio-inspired WSNs based on the topology of GRNs. Our approach embeds the physical communication graph of the WSN into the GRN graph under the optimization criterion of minimizing the interference between different nodes. Furthermore, we propose an algorithm to identify data collection points (i.e., sinks) and improve robustness by maximizing the expansion of the network. Through an analytical evaluation, we show that our bio-inspired graph embedding approach leads to robust WSNs which preserve the structural properties of GRNs.

In most wireless sensor network (WSN) applications, data are typically gathered by sensor nodes and reported to a data collection point called sink. To support such a data collection pattern, a tree structure rooted at the sink is defined. Depending on various factors, including the WSN topology and the availability of resources, the energy consumption of nodes in different paths of the data collection tree may vary largely, thus affecting the overall network lifetime. This paper addresses the problem of lifetime maximization of WSNs based on data collection trees. Specifically, we propose a novel and efficient algorithm, called Randomized Switching for Maximizing Lifetime (RaSMaLai), that aims at extending the lifetime of WSNs through load balancing. Given an initial data collection tree, RaSMaLai randomly switches some sensor nodes from their original paths to other paths with lower load. We prove that, under appropriate settings of the operating parameters, RaSMaLai converges with a low time complexity. We further design a distributed version of our algorithm. Through an extensive performance evaluation study that includes simulation of large-scale scenarios and real experiments on a WSN testbed, we show that the proposed RaSMaLai algorithm and its distributed version achieve a longer network lifetime than the state-of-the-art solutions.

Virtual machine (VM) placement is the process of selecting the most suitable server in large cloud data centers to deploy newly-created VMs. Several approaches have been proposed to find a solution to this problem. However, most of the existing solutions only consider a limited number of resource types, thus resulting in unbalanced load or in the unnecessary activation of physical servers. In this article, we propose an algorithm, called Max-BRU, that maximizes the resource utilization and balances the usage of resources across multiple dimensions. Our algorithm is based on multiple resource-constraint metrics that help to find the most suitable server for deploying VMs in large cloud data centers. The proposed Max-BRU algorithm is evaluated by simulations based on synthetic datasets. Experimental results show two major improvements over the existing approaches for VM placement. First, Max-BRU increases the resource utilization by minimizing the amount of physical servers used. Second, Max-BRU effectively balances the utilization of multiple types of resources.

Resources used in a cloud data center could be spread across a large number of servers that are not fully utilized. This situation results in significant operational costs which are directly related to the power consumption of active servers. Virtual machine migration enables reducing the number of active servers by consolidating the load on a limited amount of nodes. Several schemes have actually been proposed to consolidate virtual machines on the minimum number of physical servers in order to reduce power consumption. However, most of the existing solutions only consider a limited trade off among multiple types of resources, thus resulting in unnecessarily activated physical servers. This article proposes a multi-resource selection (MRS) scheme for consolidating virtual machines in cloud data centers. With MRS, each physical server is first characterized in terms of multiple types of resources and then classified through its overall resource utilization. Based on the MRS scheme, a balanced multiple-resource utilization algorithm is also used to spread the load across different types of resources while consolidating virtual machines. The proposed solution is evaluated through simulations on both synthetic and real-world workloads. Experimental results show that the proposed approach outperforms several existing schemes in terms of the number of active physical servers and the utilization of multiple resources.

Eventually, all printed signs and bulletins will be replaced by electronic displays, which are wirelessly connected to the Internet and cloud-based services. Deploying such ubiquitous displays can be cumbersome since they need to be correctly configured and authorized to access both the Internet and the necessary services, despite the fact that they have minimal input capabilities and may be in inaccessible locations. Our goal is to enable easy and secure configuration of ubiquitous displays such as digital signage and advertisements, which are managed by cloud services and show HTML5 content. In our solution, the display shows a QR code which, when scanned by the user with a camera phone, allows automatic configuration of the wireless network along with the content to be shown. This is accomplished by a long-term trust relation configured between the cloud service and the wireless access network. We build on existing technologies and standard protocols, including RADIUS and EAP, without requiring new software to be installed on the phone or changes to the network infrastructure.

In most wireless sensor network (WSN) applications, data are typically gathered by the sensor nodes and reported to a data collection point, called the sink. In order to support such data collection, a tree structure rooted at the sink is usually defined. Based on different aspects, including the actual WSN topology and the available energy budget, the energy consumption of nodes belonging to different paths in the data collection tree may vary significantly. This affects the overall network lifetime, defined in terms of when the first node in the network runs out of energy. In this paper, we address the problem of lifetime maximization of WSNs in the context of data collection trees. In particular, we propose a novel and efficient algorithm, called Randomized Switching for Maximizing Lifetime (RaSMaLai) that aims at maximizing the lifetime of WSNs through load balancing with a low time complexity. We further design a distributed version of our algorithm, called D-RaSMaLai. Simulation results show that both the proposed algorithms outperform several existing approaches in terms of network lifetime. Moreover, RaSMaLai offers lower time complexity while the distributed version, D-RaSMaLai, is very efficient in terms of energy expenditure.

Convergecast is a typical form of data collection in wireless sensor networks (WSNs), wherein nodes sample data from the environment and send them to a common destination. In order to prolong the network lifetime, a duty-cycle mechanism is usually coupled with a routing tree structure, in which nodes are organized in clusters. Each cluster aggregates data and sends them towards the root of the tree. However, clusters can interfere each other if their active time is not properly chosen. Furthermore, scheduling can lead to a long data delivery delay when a duty-cycle mechanism is used. In this article, we introduce a receiver-oriented scheduling algorithm for cluster-tree WSNs which provides a bounded latency for convergecast data collection. In contrast with most of the existing works in the literature, where two nodes are assumed to interfere if they are at most 2 hops away, we address the more general and realistic case where interfering nodes can be up to t hops away from each other, where te2. We first show that the minimum-latency convergecast problem is NP-hard for cluster-based WSNs with arbitrary topologies. We then focus on tree-based WSNs and derive bounds on the latency for convergecast data collection. We also propose a heuristic to obtain a t-interference-free scheduling in O(nt) time, where n is the number of clusters in the WSN. We finally validate our findings by simulation on both synthetic topologies and routing trees obtained from WSN deployments.

In a pervasive system, users have very dynamic and rich interactions with the environment and its elements, including other users. To efficiently support users in such environments, a high-level representation of the system (namely, context) is usually exploited. However, since pervasive environments are inherently people-centric, context might consist of sensitive information. As a consequence, privacy concerns arise, especially in terms of how to control information disclosure to third parties (e.g., other users). In this paper we propose context-aware approaches to privacy preservation in wireless and mobile pervasive environments. Specifically, we design two schemes: (i) to reduce the interactions between the user and the system, and (ii) to exploit the interactions between different users. Both of our solutions are adaptive, thus suitable for dynamic scenarios. In addition, our schemes require limited computational and storage resources, so that they can be implemented on resource-constrained personal and sensing devices. We apply our solutions to a smart healthcare scenario, and show that our schemes not only effectively protect the user privacy, but also significantly reduce the interactions with the system, thus improving the user experience.

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Urban sensing is emerging as a significant Wireless Sensor Networks (WSNs) application. In such a scenario, static sensors are sparsely deployed in an urban area to collect environmental information. Sensed data are opportunistically collected by Mobile Sinks (MSs), which can be other sensor nodes attached to cars or buses, or carried by people while they move around the city. Since the contacts between the MSs and the static sensors are infrequent and short, reliable and energy efficient data collection is a primary concern of such applications. To this end, we exploit a hybrid data delivery scheme based on both Erasure Coding (EC) and feedback by the MSs. We provide an optimized implementation, and show by extensive experiments in a real testbed that the proposed approach is feasible, despite the very limited storage and processing resources of commercially available sensor platforms.

It has been shown that sink mobility provides an energy-efficient approach to data delivery in wireless sensor networks (WSNs). Most of the approaches targeted to WSNs with mobile sinks (MSs) addressed the problem of data delivery where only a few messages are reported during a long time frame. However, transmitting streaming data is becoming relevant in WSNs, as more and more multimedia sensor nodes - equipped with image, audio, and video capabilities - are being used to characterize the sensing environment. In this scenario, a sequence of messages propagates into the network, hence the problem of finding an effective routing path for delivering data to MSs becomes even more challenging, since the communication overhead for reaching the MS might also be significant. In this paper, we present an energy-efficient streaming data delivery (SDD) protocol for cluster-based WSNs with MSs. Different from existing works, we focus on the mobility support for the delivery of streaming data in hierarchical WSNs. By introducing a cross-cluster handover mechanism and a path redirection scheme, SDD maintains the end-to-end connectivity between the source and the MS, while avoiding the constant transmission of the MS location as it moves across multiple clusters. We evaluate the performance of the proposed SDD protocol, and compare it with a hierarchical cluster-based data dissemination protocol. Simulation results demonstrate its effectiveness, in terms of both end-to-end delivery delay and energy-efficiency.

The availability and diffusion of wireless sensor nodes, personal communication devices (e.g., smartphones), as well as application-specific devices (e.g., surveillance cameras) has changed the typical sensing application scenarios where data are collected from the environment for the purpose of monitoring a phenomenon and detecting events. The combination of highly heterogeneous devices, in terms of sensing, processing, and communication capabilities, has become a key feature to collaborative, distributed, and multimodal sensing applications. However, the heterogeneity of devices also raises a number of challenges for the application developers. In this paper, we present a general software framework for heterogeneous and multimedia wireless sensor networks. The framework abstracts from the individual sensing devices and platforms, and enables collaborative and distributed sensing applications. We present a reference application scenario represented by Assisted Living Environments (ALEs).We show the potential of our proposed framework by a preliminary testbed implementation consisting in a multimodal application for fall detection of elderly people.

A major concern in wireless sensor networks (WSNs) is energy conservation, since battery-powered sensor nodes are expected to operate autonomously for a long time, e.g., for months or even years. Another critical aspect of WSNs is reliability, which is highly application-dependent. In most cases it is possible to trade-off energy consumption and reliability in order to prolong the network lifetime, while satisfying the application requirements. In this paper we propose an adaptive and cross-layer framework for reliable and energy-efficient data collection in WSNs based on the IEEE 802.15.4/ZigBee standards. The framework involves an energy-aware adaptation module that captures the application's reliability requirements, and autonomously configures the MAC layer based on the network topology and the traffic conditions in order to minimize the power consumption. Specifically, we propose a low-complexity distributed algorithm, called ADaptive Access Parameters Tuning (ADAPT), that can effectively meet the application-specific reliability under a wide range of operating conditions, for both single-hop and multi-hop networking scenarios. Our solution can be integrated into WSNs based on IEEE 802.15.4/ZigBee without requiring any modification to the standards. Simulation results show that ADAPT is very energy-efficient, with near-optimal performance.

Wireless Sensor Networks (WSNs) represent a very promising solution in the field of wireless technologies for industrial applications. However, for a credible deployment of WSNs in an industrial environment, four main properties need to be fulfilled, i.e., energy efficiency, scalability, reliability, and timeliness. In this paper, we focus on IEEE 802.15.4 WSNs and show that they can suffer from a serious unreliability problem. This problem arises whenever the power management mechanism is enabled for energy efficiency, and results in a very low packet delivery ratio, also when the number of sensor nodes in the network is very low (e.g., 5). We carried out an extensive analysis-based on both simulation and experiments on a real WSN-to investigate the fundamental reasons of this problem, and we found that it is caused by the contention-based Medium Access Control (MAC) protocol used for channel access and its default parameter values. We also found that, with a more appropriate MAC parameters setting, it is possible to mitigate the problem and achieve a delivery ratio up to 100%, at least in the scenarios considered in this paper. However, this improvement in communication reliability is achieved at the cost of an increased latency, which may not be acceptable for industrial applications with stringent timing requirements. In addition, in some cases this is possible only by choosing MAC parameter values formally not allowed by the standard.

Wireless sensor networks (WSNs) have emerged as an effective solution for a wide range of applications. Most of the traditional WSN architectures consist of static nodes which are densely deployed over a sensing area. Recently, several WSN architectures based on mobile elements (MEs) have been proposed. Most of them exploit mobility to address the problem of data collection in WSNs. In this article we first define WSNs with MEs and provide a comprehensive taxonomy of their architectures, based on the role of the MEs. Then we present an overview of the data collection process in such a scenario, and identify the corresponding issues and challenges. On the basis of these issues, we provide an extensive survey of the related literature. Finally, we compare the underlying approaches and solutions, with hints to open problems and future research directions.

Wireless Sensor Networks (WSNs) are a very appealing solution for many practical applications. Recently, WSNs have also been deployed in industrial scenarios, even for critical applications. Two major requirements are needed for an effective deployment of WSNs in such scenarios. The first is energy efficiency, as a network lifetime in the order of months or years is usually required. The other is reliability, since an even moderate message loss cannot be tolerated in critical applications. In this paper we evaluate the performance of the IEEE 802.15.4 standard in multi-hop WSNs where sleep/wakeup scheduling protocols are used for energy conservation. We show through extensive simulation results that the MAC parameter settings significantly impact on the performance. We demonstrate how an appropriate tuning of the MAC parameters can improve the reliability of communications, resulting in a very high delivery ratio. In addition, our solution also obtains a low energy expenditure.

In recent years, the number of sensor network deployments for real-life applications has rapidly increased and it is expected to expand even more in the near future. Actually, for a credible deployment in a real environment three properties need to be fulfilled, i.e., energy efficiency, scalability and reliability. In this paper we focus on IEEE 802.15.4 sensor networks and show that they can suffer from a serious MAC unreliability problem, also in an ideal environment where transmission errors never occur. This problem arises whenever power management is enabled - for improving the energy efficiency - and results in a very low delivery ratio, even when the number of nodes in the network is very low (e.g., 5). We carried out an extensive analysis, based on simulations and real measurements, to investigate the ultimate reasons of this problem. We found that it is caused by the default MAC parameter setting suggested by the 802.15.4 standard. We also found that, with a more appropriate parameter setting, it is possible to achieve the desired level of reliability (as well as a better energy efficiency). However, in some scenarios this is possible only by choosing parameter values formally not allowed by the standard.

In recent years, the use of wireless sensor networks for industrial applications has rapidly increased. However, energy consumption still remains one of the main limitations of this technology. As communication typically accounts for the major power consumption, the activity of the transceiver should be minimized, in order to prolong the network lifetime. To this end, this paper proposes an adaptive staggered sleep protocol (ASLEEP) for efficient power management in wireless sensor networks targeted to periodic data acquisition. This protocol dynamically adjusts the sleep schedules of nodes to match the network demands, even in time-varying operating conditions. In addition, it does not require any a priori knowledge of the network topology or traffic pattern. ASLEEP has been extensively studied with simulation. The results obtained show that, under stationary conditions, the protocol effectively reduces the energy consumption of sensor nodes (by dynamically adjusting their duty-cycle to current needs) thus increasing significantly the network lifetime. With respect to similar nonadaptive solutions, it also reduces the average message latency and may increase the delivery ratio. Under time-varying conditions, the protocol is able to adapt the duty-cycle of single nodes to the new operating conditions, while keeping a consistent sleep schedule among sensor nodes. The results presented here are also confirmed by an experimental evaluation in a real testbed.

Wireless sensor networks (WSNs) have emerged as the enabling technology for a wide range of applications. In the context of environmental monitoring, especially in urban scenarios, a mobile data collector (data mule) can be exploited to get data sensed by a number of nodes sparsely deployed in the sensing field. In this paper we describe and analyze protocols for reliable and energy-efficient data collection in WSNs with data mules. Our main contribution is the joint performance analysis of the discovery and the data transfer phases of the data collection process. Our results show that a low duty cycle (i.e. in the order of 1%) is actually feasible for most common environmental monitoring applications. We also found that, depending on the mobility pattern of the data mule, a lower duty cycle may not be always a more convenient option for energy efficiency. Based on these results, we outline in the paper possible directions for improving the energy efficiency of data collection in sparse WSNs with data mules.

In the last years wireless sensor networks (WSN) have emerged as an enabling technology for a wide range of applications. The main challenge in the deployment and actual utilization of WSNs is the scarce energy budget available at sensor nodes. In this paper we address this problem through an Adaptive Staggered Sleep Protocol (ASLEEP) which is suitable to environmental monitoring applications. By tuning dynamically the wakeup period of each sensor node to its current traffic pattern, ASLEEP reduces both energy consumption and message latency. In addition, it can adapt to changes in the operating conditions. We present an experimental evaluation of ASLEEP based on a prototype implementation in a real testbed. The experimental results show that the proposed solution provides better performance, in terms of reduced energy consumption and message latency, in comparison with other similar approaches.