• Scanned IPv4 to find 14386 MQTT brokers and captured 3.2 billion messages • Few brokers generate most traffic (“elephants”), while many handle little (“mice”). • Brokers show low load overall, with heavy-tailed traffic patterns. • Over 88% of messages use QoS 0, prioritizing low-latency over delivery guarantees. • Topics and payloads mostly follow best practices, but outliers reveal inefficiencies. The Message Queue Telemetry Transport (MQTT) protocol is widely used in Internet of Things (IoT) applications, offering a lightweight and efficient communication model for resource-constrained devices. Despite its increasing adoption in domains such as smart homes, industrial automation, and environmental monitoring, large-scale empirical studies on MQTT traffic are rare, and existing work often focuses on controlled experiments rather than natural, in-the-wild deployments. In this paper, we address this gap by analyzing MQTT traffic “in the wild”. We developed a measurement framework to scan the IPv4 address space, identifying 14,386 active brokers. We collected about 3.2 billion messages over two weeks, enabling an in-depth study of broker throughput, topic structures, payload composition, and QoS configurations. Our findings reveal that broker throughput is generally low, suggesting limited stress in real-world usage. Topic structures vary significantly, with some brokers using deep hierarchies, which may impact distributed deployments. Structured payloads (JSON, Strings) dominate MQTT traffic, presenting opportunities for broker-side optimizations. Furthermore, QoS 0 is overwhelmingly preferred, indicating a focus on low-latency communication over reliability guarantees. These insights contribute to a better understanding of MQTT traffic patterns, which can be leveraged for protocol optimizations, scalability strategies, and security considerations for future IoT deployments.

The Internet of Things (IoT) is revolutionizing connectivity by enabling everyday objects and devices to communicate over the Internet, streamlining industrial processes, improving quality of life, and driving innovation across various applications. Ensuring seamless communication between heterogeneous IoT devices remains a fundamental challenge due to differences in underlying communication protocols. This work presents a broker-based extension designed to enable direct, bidirectional interoperability between MQTT and CoAP, two of the most widely adopted IoT protocols. Unlike middleware-based solutions, this approach integrates RESTful and publish/subscribe paradigms within a single broker architecture, streamlining deployment and minimizing system complexity. The proposed solution demonstrates low-latency performance and minimal resource overhead with low median latencies of 2.15 ms and an estimated increase of only 0.53 ms over the baseline. Resource-wise, the broker extension shows a modest median CPU overhead of 2.6% and a memory usage increase of just 0.1% at higher throughputs. These results confirm that the system can efficiently manage multiple concurrent message flows and maintain scalability under varying traffic conditions. This position the broker extension as a practical, modular, and lightweight solution for enhancing IoT interoperability in constrained environments.

The rapid proliferation of Internet of Things (IoT) devices across diverse sectors has given rise to the field of IoT Forensics, which focuses on analyzing digital traces of IoT appliances for legally significant insights. This field extends traditional digital forensic methods to address the unique characteristics of IoT devices, with the goal of identifying security breaches and reconstructing human activities based on data retrieved from IoT systems. Due to the limited memory and processing capabilities of IoT devices, innovative methods for data collection and analysis are required. As an example, in the Smart Home or Smart Office scenarios intermediate network devices such as Wi-Fi access points may be leveraged for such goals, including monitoring and analysis of IoT network traffic. In this context, this paper proposes a resource optimization model for forensics tasks based on network traffic monitoring and analysis on consumer Wi-Fi access points. The model maximises the expected performance achieved for forensic tasks while balancing the storage and processing capabilities required for data collection in Wi-Fi access points. The proposed model can determine the optimal aggregation window used to group network packets for traffic analysis, the number of statistical features to extract from such packets and the bits per feature to use for data storage, in order to achieve optimal accuracy and maintain low impact on the computing device. Experimental results demonstrate the models efficacy in constrained environments, allowing us to decide on the resource allocation in network devices when a high number of tasks is involved.

Monitoring business processes within complex supply chains demands efficient data collection and analytics tailored to diverse phenomena. Traditional centralized solutions face limitations in adapting to the dynamic nature of supply chains. This calls for distributed solutions which break the usual architectural assumption to have a central entity in charge of collecting, integrating and offering tools for the analysis. This project, embedded in a larger initiative called MICS, proposes an inno-vative distributed monitoring solution integrating blockchain for a trustworthy and efficient data analytics strategy that preserves data sovereignty in complex collaborative environments. Leveraging the cloud -edge continuum, the solution aims to ensure secure data exchange, adherence to agreements, and real-time analytics. Expected outcomes include an innovative federated architecture, 5G slice management solutions, an adversarial analysis of supply chain security, and a proof-of-concept implementation of the blockchain-based data flow tracking system. These developments aim to enhance the reliability, security, and efficiency of supply chain monitoring in dynamic industrial environments.

MAC randomization is a widely used technique implemented on most modern smartphones to protect user’s privacy against tracking based on Probe Request frames capture. However, there exist weaknesses in such a methodology which may still expose distinctive information, allowing to track the device generating the Probe Requests. Such techniques, known as MAC de-randomization algorithms, generally exploit Information Elements (IEs) contained in the Probe Requests and use clustering methodologies to group together frames belonging to the same device. While effective on heterogeneous device types, such techniques are not able to differentiate among devices of identical type and running the same Operating System (OS). In this paper, we propose a MAC de-randomization technique able to overcome such a weakness. First, we propose a new dataset of Probe Requests captured from devices sharing the same characteristics. Secondly, we observe that the time-frequency pattern of Probe Request emission is unique among devices and can therefore be used as a discriminative feature. We embed such a feature in a two-stage clustering methodology and show through experiments its effectiveness compared to state-of-the-art techniques based solely on IEs fingerprinting. The original dataset used in this work is made publicly available for reproducible research.

Recent advances in the Internet of Things are leading to a proliferation of smart devices in our daily life. Having so many connected devices around us potentially introduces new witnesses that can be a reference for forensic investigations. For these reasons, IoT Forensics has become a popular research area with the goal of extracting information from IoT devices to be used as potential evidence. This work presentsFeature-Sniffer, a framework to be installed in Wi-Fi access points with the aim of facilitating the extraction of network traffic information from IoT devices, to be later used for forensic purposes. The tool allows the on-the-fly computation of traffic features from connected IoT devices by using a simple user interface for its configuration. After presenting the tool logic and its implementation details, we present an accurate analysis of the tool computational impact on two different consumer Wi-Fi access points. Finally, we present four different IoT forensics use cases, in which network traffic features extracted with the proposed tool from consumer IoT devices are analyzed with machine learning techniques with the goal of 1) identifying the device producing the traffic; 2) recognizing the activity performed by the user; 3) detecting the user’s passage through a room door; and 4) detecting and classifying user interactions with a smart speaker. We conclude the work by presenting an analysis of possible storage optimization for evidence preservation with the use of lossy compression techniques.

Message queuing telemetry transport (MQTT), one of the most popular application layer protocols for the Internet of Things, works according to a publish/subscribe paradigm where clients connect to a centralized broker. Sometimes (e.g., in high scalability and low-latency applications), it is required to depart from such a centralized approach and move to a distributed one, where multiple MQTT brokers cooperate together. Many MQTT brokers (both open source or commercially available) allow to create such a distributed environment: however, it is challenging to select the right solution due to the many available choices. This article proposes, therefore benchmarking framework for distributed MQTT brokers (BORDER), a framework for creating and evaluating distributed architectures of MQTT brokers with realistic and customizable network topologies. Based on isolated Docker containers and emulated network components, the framework provides quantitative metrics about the overall system performance, such as End-to-End latency as well as network and physical resources consumed. We use BORDER to compare five of the most popular MQTT brokers that allow the creation of distributed architectures and we release it as an open-source project to allow for reproducible researches.

MQTT, one of the most popular protocols for the IoT, works according to a publish/subscribe pattern in which multiple clients connect to a single broker, generally hosted in the cloud. However, such a centralised approach does not scale well considering the massive numbers of IoT devices forecasted in the next future, thus calling for distributed solutions in which multiple brokers cooperate together. Indeed, distributed brokers can be moved from traditional cloud-based infrastructure to the edge of the network (as it is envisioned by the upcoming MEC technology of 5G cellular networks), with clear improvements in terms of latency, for example. This paper proposes MQTT-ST, a protocol able to create such a distributed architecture of brokers, organised through a spanning tree. The protocol uses in-band signalling (i.e., reuses MQTT primitives for the control messages) and allows for full message replication among brokers, as well as robustness against failures. We tested MQTT-ST in different experimental scenarios and we released it as open-source project to allow for reproducible research.

Accurate and efficient resource utilization predictions are of vital importance for the future generation of mobile wireless networks. By anticipating network resource demand, the operator can perform proactive resource allocation and predictive network control to improve network resource efficiency. In this paper, we exploit deep and transfer learning algorithms for multi-step resource utilization prediction in radio networks. In particular, we propose long short-term memory network-based architectures with transfer learning for the multi-step prediction task, in order to address scalability, computation time and data storage limitations of current implementations for large-scale networks. We carry out extensive experiments on a dataset collected from an LTE field network. When predicting physical resource block percentage utilization, our approach achieves state of the art results with root mean square error below 12 for a four-hour-ahead prediction, in half of the computation time required by deep learning methods without transfer learning.

Standard LoRaWANs leverage pure ALOHA at the medium access control layer, which is proved to be a performance bottleneck as the network size scales up. Stimulated by this fact, this paper studies the applicability and the performance of listen before talk (LBT) medium access schemes in the context of LoRaWANs. We consider two different implementations of LBT: physical layer LBT based on energy detection only and MAC layer LBT based on layer 2 frame decoding, and we propose a Markovian framework to evaluate the performance of LoRaWANs under such setting in terms of data extraction rate and average delay experienced by transmitted uplink messages. The proposed framework is also leveraged to assess the performance of “mixed” LoRaWAN scenarios, where some devices access the channel according to the standard-compliant ALOHA protocol, while other devices transmit according to LBT.

The Message Queueing Telemetry Transport (MQTT) publish/subscribe protocol is the de facto standard at the application layer for IoT, M2M and wireless sensor networks applications. This demonstration showcases MQTT+, an advanced version of MQTT which provides an enhanced protocol syntax and enriches the broker with data filtering, processing and aggregation functionalities. Such features are ideal in all those applications in which edge devices are interested in performing processing operations over the data published by multiple clients, where using the original MQTT protocol would result in unacceptably high network bandwidth usage and energy consumption for the edge devices. MQTT+ is implemented starting from an open source MQTT broker and evaluated in different application scenarios which are demonstrated live using the Node-RED IoT prototyping framework.M

We set ourselves from the perspective of a LoRaWAN network operator and we introduce a mathematical programming framework to jointly optimize network layout and network configuration at design time. The proposed framework returns the most cost-effective network layout in terms of gateways position, gateways backhaul configuration and LoRaWAN physical parameters configuration under tight constraints of end node coverage, end-to-end message transmission latency and message extraction rate. Numerical results obtained on realistic network instances demonstrate that the proposed approach leads to network configuration with superior performance with respect to coverage-only classical design policies.

In this work we address the analysis of a LoRaWAN network where some devices access the channel according to the standard-compliant ALOHA protocol, while other devices transmit according to a Listen Before Talk paradigm based on the CSMA/CA mechanism. To analyze this scenario, we propose a mathematical model to obtain the Data Extraction Rate both for CSMA/CA and ALOHA devices, as well as the average delay experienced by messages transmitted by CSMA/CA devices. Simulation results show the accuracy of our model, as well as the benefits of introducing CSMA/CA devices into the network, even when not all the devices implement this mechanism and must coexist with ALOHA devices.

Fifth generation wireless networks (5G) will face key challenges caused by diverse patterns of traffic demands and massive deployment of heterogeneous access points. In order to handle this complexity, machine learning techniques are expected to play a major role. However, due to the large space of parameters related to network optimization, collecting data to train models for all possible network configurations can be prohibitive. In this paper, we analyze the possibility of performing a knowledge transfer, in which a machine learning model trained on a particular network configuration is used to predict a quantity of interest in a new, unknown setting. We focus on the tilt-dependent received signal strength maps as quantities of interest and we analyze two cases where the knowledge acquired for a particular antenna tilt setting is transferred to (i) a different tilt configuration of the same antenna or (ii) a different antenna with the same tilt configuration. Promising results supporting knowledge transfer are obtained through extensive experiments conducted using different machine learning models on a real dataset.

We focus on the problem of managing a shared physical wireless sensor network (WSN) where a single network infrastructure provider leases the physical resources of the networks to application providers to run/deploy specific applications/services. In this scenario, we solve jointly the problems of application admission control (AAC), that is, whether to admit the application/service to the physical network, and wireless sensor network slicing (SNS), that is, to allocate the required physical resources to the admitted applications in a transparent and effective way. We propose a mathematical programming framework to model the joint AAC-SNS problem which is then leveraged to design effective solution algorithms. The proposed framework is thoroughly evaluated on realistic WSNs infrastructures.

Sensor network virtualization enables the possibility of sharing common physical resources to multiple stakeholder applications. This paper focuses on addressing the dynamic adaptation of already assigned virtual sensor network resources to respond to time varying application demands. We propose an optimization framework that dynamically allocate applications into sensor nodes while accounting for the characteristics and limitations of the wireless sensor environment. It takes also into account the additional energy consumption related to activating new nodes and/or moving already active applications. Different objective functions related to the available energy in the nodes are analyzed. The proposed framework is evaluated by simulation considering realistic parameters from actual sensor nodes and deployed applications to assess the efficiency of the proposals.

The rapid evolution of mobile radio network technologies poses severe technical and economical challenges to mobile network operators (MNOs); on the economical side, the continuous roll-out of technology updates is highly expensive, which may lead to the extreme, where offering advanced mobile services becomes no longer affordable for MNOs which thus, are not incentivized to innovate. Mobile infrastructure sharing among MNOs becomes then an important building block to lower the required per-MNO investment cost involved in the technology roll-out and management phases. We focus on a radio access network (RAN) sharing situation where multiple MNOs with a consolidated network infrastructure coexist in a given set of geographical areas; the MNOs have then to decide if it is profitable to upgrade their RAN technology by deploying additional small-cell base stations and whether to share the investment (and the deployed infrastructure) of the new small-cells with other operators. We address such strategic problems by giving a mathematical framework for the RAN infrastructure sharing problem which returns the “best” infrastructure sharing strategies for operators (coalitions and network configuration) when varying techno-economic parameters such as the achievable throughput in different sharing configurations and the pricing models for the service offered to the users. The proposed formulation is then leveraged to analyze the impact of the aforementioned parameters/input in a realistic mobile network environment based on LTE technology.

Several computer vision tasks exploit a succinct representation of the visual content in the form of sets of local features. Given an input image, feature extraction algorithms identify keypoints and assign to each of them a descriptor, based on the characteristics of the surrounding visual content. Several tasks might require local features to be extracted from a video sequence, on a frame-by-frame basis. Although temporal downsampling has been proven to be an effective solution for mobile augmented reality and visual search, high temporal resolution is a key requirement for time-critical applications such as object tracking, event recognition, pedestrian detection, surveillance. In recent years, more and more computationally efficient visual feature detectors and descriptors have been proposed. Nonetheless, such approaches are tailored to still images. In this paper we propose a fast keypoint detection algorithm for video sequences, that exploits the temporal coherence of the sequence of keypoints. According to the proposed method, each frame is preprocessed so as to identify the parts of the input frame for which keypoint detection and description need to be performed. Our experiments show that it is possible to achieve a reduction in computational time of up to 40%, without significantly affecting the task accuracy.

Visual Sensor Networks (VSNs) have been recently used for implementing automatic visual analysis tasks where local image features, instead of images, are compressed and transmitted to a central controller. Such features may also be compressed in a multi-view fashion, exploiting the redundancy between overlapping views. In this paper we analyze the problem of multi-view coding and routing of features in VSNs. We empirically analyze the relationship between the bitrate reduction obtained with a practical multi-view local features encoder and several geometry-based, image-based and feature-based predictors. The purpose of this analysis is to identify the most accurate, yet compact predictor of the achievable compression efficiency when jointly encoding correlated streams of local features. Then, we propose a robust optimization framework that exploits the aforementioned predictors. The proposed mathematical problem maximizes the amount of data extracted from the VSN by properly routing the streams of features, subject to capacity, interference and energy constraints, explicitly considering the uncertainty in the compression efficiency estimation. Extensive experiments on simulated VSNs show that multi-view coding maximizes the amount of data extracted from camera nodes, while the robust optimization approach provides significant improvement in uncertain scenarios compared to the optimal solution of a deterministic approach.

In this work, we analyze the use of a WiFi network deployed in a large-scale technical university. To this extent, we leverage three weeks of WiFi traffic data logs and characterize the spatio-temporal correlation of the traffic at different granularities (each individual access point, groups of access points, entire network). The spatial correlation of traffic across nearby access points is also assessed. Then, we search for distinctive fingerprints left on the WiFi traffic by different situations/conditions; namely, we answer the following questions: Do students attending a lecture use the wireless network in a different way than students not attending a lecture?, and Is there any difference in the usage of the wireless network during architecture or engineering classes? A supervised learning approach based on Quadratic Discriminant Analysis (QDA) is used to classify empty vs. occupied rooms and engineering vs. architecture lectures using only WiFi traffic logs with promising results.

We propose a method for classifying Wi-Fi enabled mobile handheld devices (smartphones) and non-handheld devices (laptops) in a completely passive way, that is resorting neither to traffic probes on network edge devices nor to deep packet inspection techniques to read application layer information. Instead, classification is performed starting from probe requests Wi-Fi frames, which can be sniffed with inexpensive commercial hardware. We extract distinctive features from probe request frames (how many probe requests are transmitted by each device, how frequently, etc.) and take a machine learning approach, training four different classifiers to recognize the two types of devices. We compare the performance of the different classifiers and identify a solution based on a Random Decision Forest that correctly classify devices 95% of the times. The classification method is then used as a pre-processing stage to analyze network traffic traces from the wireless network of a university building, with interesting considerations on the way different types of devices uses the network (amount of data exchanged, duration of connections, etc.). The proposed methodology finds application in many scenarios related to Wi-Fi network management/optimization and Wi-Fi based services.

We present a complete, open-source framework for rapid experimentation of visual sensor network (VSN) solutions. From the software point of view, we base our architecture on open-source and widely known C++ libraries to provide the basic image processing and networking primitives. The resulting system can be leveraged to create different types of VSNs, characterized by the presence of multiple cameras, relays and cooperator nodes, and can be run on any Linux-based hardware platform, such as the BeagleBone Black. To demonstrate the flexibility of the proposed framework, we describe two different application scenarios typical of VSNs, namely object recognition and parking monitoring. The framework is then used to evaluate the benefits of two complementary paradigms for networked visual analysis recently discussed in the literature. In the traditional compress-then-analyze (CTA) paradigm, compressed images are transmitted from camera nodes to a central controller, where they are analyzed. In the novel analyze-then-compress (ATC) paradigm, camera nodes extract and compress local features from the acquired images. Such features are transmitted to the central controller and used to perform visual analysis. We show that the ATC paradigm outperforms CTA from the consumed energy point of view, at the same target analysis accuracy in both the application scenarios.

To accommodate the ever-growing traffic load and bandwidth demand generated by mobile users, mobile network operators (MNOs) need to frequently invest in high spectral efficiency technologies and increase their hold of spectrum resources; MNOs have then to weigh between building individual networks or entering into network and spectrum sharing agreements. We address here the problem of radio access network and spectrum sharing in 4G mobile networks by focusing on a case when multiple MNOs plan to deploy small cell base stations in a geographical area in order to upgrade their existing network infrastructure. We propose two cooperative game models (with and without transferable utility) to address the proposed problem: for given network (user throughput, MNO market, and spectrum shares) and economic (coalition cost and mobile data pricing model) settings, the proposed models output a cost division policy that guarantees coalition (sharing agreement) stability.

Visual sensor networks (VSNs) have attracted the interest of researchers worldwide in the last few years, and are expected to play a major role in the evolution of the Internet-of-Things (IoT). When used to perform visual analysis tasks, VSNs may be operated according to two different paradigms. In the traditional compress-then-analyze paradigm, images are acquired, compressed and transmitted for further analysis. Conversely, in the analyze-then-compress paradigm, image features are extracted by visual sensor nodes, encoded and then delivered to a remote destination where analysis is performed. The question this paper aims to answer is What is the best visual analysis paradigm in VSNs?To do this, first we empirically characterize the rate-energy-accuracy performance of the two aforementioned paradigms. Then, we leverage such models to formulate a resource allocation problem for VSNs. The problem optimally allocates the specific paradigm used by each camera node in the network and the related transmission source rate, with the objective of optimizing the accuracy of the visual analysis task and the VSN coverage. Experimental results over several VSNs instances demonstrate that there is no “winning” paradigm, but the best performance are obtained by allowing the coexistence of the two and by properly optimizing their utilization.

Most of the literature on delay tolerant networks (DTNs) focuses on optimal routing policies exploiting a priori knowledge about nodes mobility traces. For the case in which no a priori knowledge is available (very common in practice), apart from basic epidemic routing, the main approaches focus on controlling two-hop routing policies. However, these latter results commonly employ fluid approximation techniques, which, in principle, do not provide any theoretical bound over the approximation ratio. In our work, we focus on the case without a priori mobility knowledge and we provide approximation algorithms with theoretical guarantees that can be applied to cases where the number of hops allowed in the routing process is arbitrary. Our approach is rather flexible allowing us to address heterogeneous mobility patterns and transmission technologies, to consider explicitly the signaling and transmission costs, and to include also nodes discarding packets after a local timeout. We then provide a comprehensive performance evaluation of our algorithms, showing that two-hop routing provides the best tradeoff between delay and energy and that, in this case, they find solutions very close to the optimal ones with a low overhead. Finally, we compare our methods against some state-of-the-art approaches by means of a DTN simulation environment in realistic settings.

We propose an optimization framework to perform resource allocation in virtual sensor networks. Sensor network virtualization is a promising paradigm to improve flexibility of wireless sensor networks which allows to dynamically assign physical resources to multiple stakeholder applications. The proposed optimization framework aims at maximizing the total number of applications which can share a common physical network, while accounting for the distinguishing characteristics and limitations of the wireless sensor environment (limited storage, limited processing power, limited bandwidth, tight energy consumption requirements). The proposed framework is finally applied to realistic network topologies to assess the gain involved in letting multiple applications share a common physical network with respect to one-application, one-Network vertical design approaches.

This work considers the strategic situation which arises when Mobile Network Operators (MNOs) coexisting in a given geographical area have to decide whether to invest in new radio access technology and whether to share the investment (and the infrastructure) with other operators. We focus on heterogeneous networks (HetNet) where MNOs add a layer of small cells to their existing macro cells. We address such strategic scenario by proposing a Mixed Integer Linear Programming formulation of the infrastructure sharing problem which takes as input techno-economic parameters as the achievable throughput in different sharing configurations, the pricing models for the service offered to the end users and the expectations on the return on investment for the mobile operators, and returns as output the “best” infrastructure/investment sharing options for the MNOs. The proposed formulation is finally leveraged to analyze the dynamics involved in the infrastructure sharing process under different techno-economic conditions in realistic network scenarios.

This work focuses on Visual Sensor Networks (VSNs) which perform visual analysis tasks such as object recognition. There, the goal is to find the image in a reference database which is the closest match to the image captured by camera sensor nodes. Recognition is performed by relying on visual features extracted from the acquired image, which are matched against a database of labeled features in order to find the closest image match. The matching functionalities are often implemented at a central controller outside the VSN. In contrast, we study the performance trade-offs involved in distributing the matching functionalities inside the VSN by letting sensor nodes performing parts of the matching process. We propose an optimization framework to optimally distribute the matching task to in-network sensor nodes with the goal of minimizing the overall completion time of the recognition task. The proposed optimization framework is then used to assess the performance of distributed matching, comparing it to a traditional, centralized approach in realistic VSN scenarios.

Distributed visual analysis applications, such as mobile visual search or Visual Sensor Networks (VSNs) require the transmission of visual content on a bandwidth-limited network, from a peripheral node to a processing unit. Traditionally, a “Compress-Then-Analyze” approach has been pursued, in which sensing nodes acquire and encode the pixel-level representation of the visual content, that is subsequently transmitted to a sink node in order to be processed. This approach might not represent the most effective solution, since several analysis applications leverage a compact representation of the content, thus resulting in an inefficient usage of network resources. Furthermore, coding artifacts might significantly impact the accuracy of the visual task at hand. To tackle such limitations, an orthogonal approach named “Analyze-Then-Compress” has been proposed [1]. According to such a paradigm, sensing nodes are responsible for the extraction of visual features, that are encoded and transmitted to a sink node for further processing. In spite of improved task efficiency, such paradigm implies the central processing node not being able to reconstruct a pixel-level representation of the visual content. In this paper we propose an effective compromise between the two paradigms, namely “Hybrid-Analyze-Then-Compress” (HATC) that aims at jointly encoding visual content and local image features. Furthermore, we show how a target tradeoff between image quality and task accuracy might be achieved by accurately allocating the bitrate to either visual content or local features.

This work studies how visual analysis tasks based on feature extraction can be speeded up in the context of Visual Sensor Networks. The main catch is for the camera node to leverage the presence of neighboring sensor nodes and offload the task, thus parallelizing its execution. We propose two mathematical programming formulations for the optimal visual task offloading problem: the first one targets the minimization of the overall task completion time while enforcing energy consumption constraints onto the nodes; the second maximizes the overall sensor network lifetime subject to a temporal constraint on the task completion time. The aforementioned formulations are used to characterize the achievable speed-up and consequent energy consumption in representative visual sensor network topologies.

Binary local features represent an effective alternative to real-valued descriptors, leading to comparable results for many visual analysis tasks while being characterized by significantly lower computational complexity and memory requirements. When dealing with large collections, a more compact representation based on global features is often preferred, which can be obtained from local features by means of, e.g., the bag-of-visual word model. Several applications, including, for example, visual sensor networks and mobile augmented reality, require visual features to be transmitted over a bandwidth-limited network, thus calling for coding techniques that aim at reducing the required bit budget while attaining a target level of efficiency. In this paper, we investigate a coding scheme tailored to both local and global binary features, which aims at exploiting both spatial and temporal redundancy by means of intra- and inter-frame coding. In this respect, the proposed coding scheme can conveniently be adopted to support the analyze-then-compress (ATC) paradigm. That is, visual features are extracted from the acquired content, encoded at remote nodes, and finally transmitted to a central controller that performs the visual analysis. This is in contrast with the traditional approach, in which visual content is acquired at a node, compressed and then sent to a central unit for further processing, according to the compress-then-analyze (CTA) paradigm. In this paper, we experimentally compare the ATC and the CTA by means of rate-efficiency curves in the context of two different visual analysis tasks: 1) homography estimation and 2) content-based retrieval. Our results show that the novel ATC paradigm based on the proposed coding primitives can be competitive with the CTA, especially in bandwidth limited scenarios.

Local features represent a powerful tool which is exploited in several applications such as visual search, object recognition and tracking, etc. In this context, binary descriptors provide an efficient alternative to real-valued descriptors, due to low computational complexity, limited memory footprint and fast matching algorithms. The descriptor consists of a binary vector, in which each bit is the result of a pairwise comparison between smoothed pixel intensities. In several cases, visual features need to be transmitted over a bandwidth-limited network. To this end, it is useful to compress the descriptor to reduce the required rate, while attaining a target accuracy for the task at hand. The past literature thoroughly addressed the problem of coding visual features extracted from still images and, only very recently, the problem of coding real-valued features (e.g., SIFT, SURF) extracted from video sequences. In this paper we propose a coding architecture specifically designed for binary local features extracted from video content. We exploit both spatial and temporal redundancy by means of intra-frame and inter-frame coding modes, showing that significant coding gains can be attained for a target level of accuracy of the visual analysis task.

Local visual features are commonly adopted to accomplish analysis tasks such as object recognition/tracking and image retrieval. Recently, several visual features extraction algorithms tailored to low-power architectures have been proposed, in order to enable image analysis on energy-constrained devices such as smart-phones or Visual Sensor Networks (VSN). In this work, we dissect and analyze BRISK, a state-of-the-art low-power visual feature extractor, in order to evaluate the impact of its individual building blocks on the overall energy consumption. For each building block, we propose a solution to limit the energy consumption without affecting the overall analysis performance. The resulting BRISKOLA (BRISK Optimized for Low-power ARM architectures) feature extractor exhibits energy savings up to 30% with respect to the original implementation.

This demo showcases some of the results obtained by the GreenEyes project, whose main objective is to enable visual analysis on resource-constrained multimedia sensor networks. The demo features a multi-hop visual sensor network operated by BeagleBones Linux computers with IEEE 802.15.4 communication capabilities, and capable of recognizing and tracking objects according to two different visual paradigms. In the traditional compress-then-analyze (CTA) paradigm, JPEG compressed images are transmitted through the network from a camera node to a central controller, where the analysis takes place. In the alternative analyze-then-compress (ATC) paradigm, the camera node extracts and compresses local binary visual features from the acquired images (either locally or in a distributed fashion) and transmits them to the central controller, where they are used to perform object recognition/tracking. We show that, in a bandwidth constrained scenario, the latter paradigm allows to reach better results in terms of application frame rates, still ensuring excellent analysis performance.

A robust hash, or content-based fingerprint, is a succinct representation of the perceptually most relevant parts of a multimedia object. A key requirement of fingerprinting is that elements with perceptually similar content should map to the same fingerprint, even if their bit-level representations are different. In this work we propose BAMBOO (Binary descriptor based on AsymMetric pairwise BOOsting), a binary local descriptor that exploits a combination of content-based fingerprinting techniques and computationally efficient filters (box filters, Haar-like features, etc.) applied to image patches. In particular, we define a possibly large set of filters and iteratively select the most discriminative ones resorting to an asymmetric pair-wise boosting technique. The output values of the filtering process are quantized to one bit, leading to a very compact binary descriptor. Results show that such descriptor leads to compelling results, significantly outperforming binary descriptors having comparable complexity (e.g., BRISK), and approaching the discriminative power of state-of-the-art descriptors which are significantly more complex (e.g., SIFT and BinBoost).

Vehicular networks, besides supporting safety-oriented applications, are nowadays expected to provide effective communication infrastructure also for supporting leisure-oriented application including content sharing, gaming and Internet access on the move. This work focuses on Vehicle to Infrastructure (V2I) scenarios, where multiple content providers own a physical infrastructure of Road Side Units (RSUs) which they use to sell contents to moving vehicles. Content provider/RSU owners compete by adapting their pricing strategies with the selfish objective to maximize their own revenues. We study the economics of the price competition between the providers by resorting to game theoretic tools. Namely, we formalize a simultaneous price game among the operators further studying the existence of Nash equilibria and their related quality in terms of Price of Anarchy and Price of Stability. The proposed game model is finally used to assess the impact onto the game equilibra of several practical factors including the vehicles' willingness to pay, the traffic densities, and the configuration of the physical networks of RSUs.

Wireless visual sensor networks (VSNs) are expected to play a major role in future IEEE 802.15.4 personal area networks (PANs) under recently established collision-free medium access control (MAC) protocols, such as the IEEE 802.15.4e-2012 MAC. In such environments, the VSN energy consumption is affected by a number of camera sensors deployed (spatial coverage), as well as a number of captured video frames of which each node processes and transmits data (temporal coverage). In this paper we explore this aspect for uniformly formed VSNs, that is, networks comprising identical wireless visual sensor nodes connected to a collection node via a balanced cluster-tree topology, with each node producing independent identically distributed bitstream sizes after processing the video frames captured within each network activation interval. We derive analytic results for the energy-optimal spatio-temporal coverage parameters of such VSNs under a priori known bounds for the number of frames to process per sensor and the number of nodes to deploy within each tier of the VSN. Our results are parametric to the probability density function characterizing the bitstream size produced by each node and the energy consumption rates of the system of interest. Experimental results are derived from a deployment of TelosB motes and reveal that our analytic results are always within 7% of the energy consumption measurements for a wide range of settings. In addition, results obtained via motion JPEG encoding and feature extraction on a multimedia subsystem (BeagleBone Linux Computer) show that the optimal spatio-temporal settings derived by our framework allow for substantial reduction of energy consumption in comparison with ad hoc settings.

Visual features are successfully exploited in several applications (e.g., visual search, object recognition and tracking, etc.) due to their ability to efficiently represent image content. Several visual analysis tasks require features to be transmitted over a bandwidth-limited network, thus calling for coding techniques to reduce the required bit budget, while attaining a target level of efficiency. In this paper, we propose, for the first time, a coding architecture designed for local features (e.g., SIFT, SURF) extracted from video sequences. To achieve high coding efficiency, we exploit both spatial and temporal redundancy by means of intraframe and interframe coding modes. In addition, we propose a coding mode decision based on rate-distortion optimization. The proposed coding scheme can be conveniently adopted to implement the analyze-then-compress (ATC) paradigm in the context of visual sensor networks. That is, sets of visual features are extracted from video frames, encoded at remote nodes, and finally transmitted to a central controller that performs visual analysis. This is in contrast to the traditional compress-then-analyze (CTA) paradigm, in which video sequences acquired at a node are compressed and then sent to a central unit for further processing. In this paper, we compare these coding paradigms using metrics that are routinely adopted to evaluate the suitability of visual features in the context of content-based retrieval, object recognition, and tracking. Experimental results demonstrate that, thanks to the significant coding gains achieved by the proposed coding scheme, ATC outperforms CTA with respect to all evaluation metrics.

In dynamic-frame Aloha, subsequent frame lengths must be optimally chosen to maximize throughput. When the initial population size N is known, numerical evaluations show that the maximum efficiency is achieved by setting the frame length equal to the backlog size at each subsequent frame; however, to the best of our knowledge, a formal proof of this result is still missing, and is provided here. As byproduct, we also prove that the asymptotic efficiency in the optimal case is e-1, provide tight upper and lower bounds for the length of the entire transmission period, and show that its asymptotic behavior is ~ne-ζ ln(n) with ζ =-0.5/\ln (1-e-1).

Visual features provide a convenient representation of the image content, which is exploited in several applications, e.g., visual search, object tracking, etc. In several cases, visual features need to be transmitted over a bandwidth-limited network, thus calling for coding techniques to reduce the required rate, while attaining a target efficiency for the task at hand. Although the literature has recently addressed the problem of coding local features extracted from still images, in this paper we propose, for the first time, a coding architecture designed for local features extracted from video content. We exploit both spatial and temporal redundancy by means of intra-frame and inter-frame coding modes. In addition, we propose a coding mode decision based on rate-distortion optimization. Experimental results demonstrate that, in the case of SIFT descriptors, exploiting temporal redundancy leads to substantial gains in terms of coding efficiency.

Binary descriptors have recently emerged as low-complexity alternatives to state-of-the-art descriptors such as SIFT. The descriptor is represented by means of a binary string, in which each bit is the result of the pair-wise comparison of smoothed pixel values properly selected in a patch around each keypoint. Previous works have focused on the construction of the descriptor neglecting the opportunity of performing lossless compression. In this paper, we propose two contributions. First, design an entropy coding scheme that seeks the internal ordering of the descriptor that minimizes the number of bits necessary to represent it. Second, we compare different selection strategies that can be adopted to identify which pair-wise comparisons to use when building the descriptor. Unlike previous works, we evaluate the discriminative power of descriptors as a function of rate, in order to investigate the trade-offs in a bandwidth constrained scenario.

We compare two paradigms for image analysis in visual sensor networks (VSN). In the compress-then-analyze (CTA) paradigm, images acquired from camera nodes are compressed and sent to a central controller for further analysis. Conversely, in the analyze-then-compress (ATC) approach, camera nodes perform visual feature extraction and transmit a compressed version of these features to a central controller. We focus on state-of-the-art binary features which are particularly suitable for resource-constrained VSNs, and we show that the “winning” paradigm depends primarily on the network conditions. Indeed, while the ATC approach might be the only possible way to perform analysis at low available bitrates, the CTA approach reaches the best results when the available bandwidth enables the transmission of high-quality images.

This interactive demo presents and compares two different paradigms for image analysis in visual sensor networks (VSN), using a testbed based on battery-operated Beagle-Bone platforms with sight and wireless communication capabilities.

Wireless access networks are often characterized by the interaction of different end users, communication technologies, and network operators. This paper analyzes the dynamics among these "actorsâ by focusing on the processes of wireless network selection, where end users may choose among multiple available access networks to get connectivity, and resource allocation, where network operators may set their radio resources to provide connectivity. The interaction among end users is modeled as a noncooperative congestion game, where players (end users) selfishly select the access network that minimizes their perceived selection cost. A method based on mathematical programming is proposed to find Nash equilibria and characterize their optimality under three cost functions, which are representative of different technological scenarios. System level simulations are then used to evaluate the actual throughput and fairness of the equilibrium points. The interaction among end users and network operators is then assessed through a two-stage multileader/multifollower game, where network operators (leaders) play in the first stage by properly setting the radio resources to maximize their users, and end users (followers) play in the second stage the aforementioned network selection game. The existence of exact and approximated subgame perfect Nash equilibria of the two-stage game is thoroughly assessed and numerical results are provided on the "qualityâ of such equilibria.

Cognitive radio networks (CRNs) are composed of frequency-agile radio devices that allow licensed (primary) and unlicensed (secondary) users to coexist, where secondary users opportunistically access channels without interfering with the operation of primary ones. From the perspective of secondary users, spectrum availability is a time-varying network resource over which multihop end-to-end connections must be maintained. In this paper, a theoretical outlook on the problem of routing secondary user flows in a CRN is provided. The investigation aims to characterize optimal sequences of routes over which a secondary flow is maintained. The optimality is defined according to a novel metric that considers the maintenance cost of a route as channels, and/or links must be switched due to the primary user activity. Different from the traditional notion of route stability, the proposed approach considers subsequent path selections, as well. The problem is formulated as an integer programming optimization model. Properties of the problem are also formally introduced and leveraged to design a heuristic algorithm when information on primary user activity is not complete. Numerical results are presented to assess the optimality gap of the heuristic routing algorithm in realistic CRN scenarios.

We consider the problem of allocating the resources in a wireless sensor network, which is designed to perform visual analysis (e.g. object recognition). We depart from the traditional compress-then-analyze paradigm, in which nodes sense, compress and transmit visual data to a sink node. Instead, we study the case in which nodes extract and lossy code local features from pixel-domain representations of the sensed visual scene. The formulation of the allocation problem entails maximizing the lifetime of the visual sensor network subject to a target accuracy of the analysis task, together with energy, bandwidth and routing constraints. To this end, we contribute with the definition of a rate-accuracy model, which plays the role of the traditional rate-distortion model commonly adopted in visual communication. The proposed model captures the impact of: i) the number of selected local features; ii) the number of bits used for quantizing local features; iii) the criterion used to select the subset of local features to be transmitted. We verify the correctness of the models on two widely adopted visual dataset and we demonstrate the network lifetime gain that can be achieved by an optimal allocation of the resources.

Efficient coding of local image descriptors is of paramount importance when they need to be transmitted to a remote destination on bandwidth constrained networks. This is a case that arises, e.g., in mobile visual search and visual wireless sensor networks. In this work we consider SURF, a popular descriptor suitable for low-complexity devices, and we provide a comparative study of lossy coding schemes operating at low bitrate (e.g., less than 128 bits / descriptor). Our investigation covers schemes that address both intra- and inter-descriptor redundancy, including methods that have not been tested before in this context, e.g., sparse coding, lifting-based coding on trees, and hybrid intra and inter-descriptor coding. The experimental evaluation is carried out on two publicly available datasets, in terms of both rate-distortion and rate-accuracy, for the specific task of object recognition. Our results show that a rate saving of 15-30% can be achieved by exploiting intra-descriptor redundancy. On the other side, addressing inter-descriptor redundancy does not lead to substantial gains when applied alone, whereas it leads to marginal gains (up to 3%) when used in hybrid schemes jointly with intra-descriptor coding.

We consider surveillance applications through wireless sensor networks (WSNs) where the areas to be monitored are fully accessible and the WSN topology can be planned a priori to maximize application efficiency. We propose an optimization framework for selecting the positions of wireless sensors to detect mobile targets traversing a given area. By leveraging the concept of path exposure as a measure of detection quality, we propose two problem versions: the minimization of the sensors installation cost while guaranteeing a minimum exposure, and the maximization of the exposure of the least-exposed path subject to a budget on the sensors installation cost. We present compact mixed-integer linear programming formulations for these problems that can be solved to optimality for reasonable-sized network instances. Moreover, we develop Tabu Search heuristics that are able to provide near-optimal solutions of the same instances in short computing time and also tackle large size instances. The basic versions are extended to account for constraints on the wireless connectivity as well as heterogeneous devices and nonuniform sensing. Finally, we analyze an enhanced exposure definition based on mobile target detection probability.

The rapid evolution of wireless technologies as well as their pervasive diffusion in everyday's life has dramatically changed the world in the last two decades. On one side, service providers/operators can nowadays leverage new wireless technologies to reach new types of customers in a cost effective way; on the other side, end users are actually given richer and more flexible “connectivity opportunities” to obtain the required services. The down side of this scenario deals with the increased complexity in the management of all the system components. Users/customers are becoming more and more demanding in terms of the perceived quality of service (QoS), eventually expecting the very same quality they experience in wired networks in the wireless realm. To this extent, advanced wireless systems must be able to support and manage high-quality services, thus matching the heterogeneous requirements coming from the users. Concurrently, risks and security threats are inherent in any wireless network, and there is an increasing demand for effective security-enforcing techniques to attain authentication, privacy, confidentiality, data integrity, privacy, access control, and non-repudiation, when operating and/or using wireless networks. In this scenario, the deployment of security measures and the provision of QoS represent two critical and tightly coupled challenges in the design of network services, architectures, and protocols in today's inter-networked world. Such challenges are even made harder in mobile wireless scenarios where the network infrastructure may feature mobile, energy and resource-constrained network devices. To this extent, the success of specific wireless networking technologies is often dependant on the capability of the specific technology to support QoS, on one side, and to provide security, on the other side. The aim of this special issue is to provide an up-to-date snapshot on advanced solutions to support the design and deployment of secure and efficient services in wireless mobile networks. The special issue is composed of seven high-quality contributions, each one focusing on a specific component of the aforementioned framework. The first batch of four papers deals with security aspects. The first two papers in the batch focus on security solutions for Wireless Mesh Networks (WMNs); namely, the first one, DSA-Mesh: a Distributed Security Architecture for WMNs, proposes a fully distributed security architecture to ensure security and data confidentiality of the communications occurring in the backbone of a WMN; the proposed solution provides a distributed authentication and access control scheme for the wireless mesh routers taking part in the network, as well as a dynamic key distribution algorithm that supports layer-2 encryption. Whilst the wireless mesh architecture addressed in the first paper is general, in the second manuscript on Differentiated Security in WMNs, the authors target the design of secure mechanisms for IEEE 802.11s-based WMNs. The rationale and main contribution of the work is the replication in the wireless environment of the virtual LAN structures typical of the wired networks to achieve protection against internal/external attacks including selective forwarding attacks, routing attacks, and eavesdropping. The third contribution, Fast 802.11 handovers with 802.1X re-authentications, targets the issue of supporting secure client mobility in multi-access point Wireless LAN deployments. Namely, the authors analyze the impact of pre-authentication and re-authentication of a mobile client when handing over between two contiguous WLAN access points, and further propose an advance algorithm to reduce the handover latency while maintaining the authentication of the client itself. The last manuscript dealing with security aspects, Characterizing The Greedy Behavior in Wireless Ad Hoc Networks, switches the focus from static wireless network (wireless mesh and wireless LANs) to dynamic wireless ad hoc networks, by analyzing the network-wide impact of greedy behaviors of single wireless nodes. The authors further introduce a rational greedy strategy in the radio resource usage which allows malicious nodes to gain higher shares of the available bandwidth. The last three contributions of this special issue focus on the provision of quality of service/quality of experience under different network scenarios and wireless technologies. The work in “Real-time support for hybrid coordinated function (HCF) controlled channel access (HCCA) function in IEEE 802.11e networks: a performance evaluation” refers to static wireless local access networks based on the IEEE 802.11e standard, and analyzes the performance of different packet scheduling techniques under the HCF controlled channel access (HCCA) operation mode. The authors propose a thorough performance evaluation through simulation of several scheduling schemes. The last two manuscripts share the same reference scenario, that is, vehicular networks. The authors of “MANET QoS Support without Reservations” propose a distributed call admission control strategy to manage the set up of in-elastic multi-hop flows over vehicular networks. The proposed solution is validated through simulation and it is further shown that it can be adapted to multicast flows as well. On the other hand, the last paper “On the quality of broadcast services in vehicular ad hoc networks” mainly addresses the broadcast transmission paradigm and provides a thorough comparative analysis of beaconless and beacon-enabled solutions for the support of heterogeneous services within suburban and highway inter-vehicular networks. To conclude, we would like to thank the Editor in Chief, Dr Hsiao Hwa Chen, for giving us the opportunity to participate in this endeavor, and for his continuous valuable advices and support. We also wish to thank the anonymous reviewers who contributed with their expertise and time to select and enhance the contributions which appear in this issue. We truly hope that the contents of this special issue will be useful for your research.

End users getting connectivity from wireless access networks want to choose the "best" access opportunity, that is, the best base station in cellular systems, and the best access point in WLANs. The metrics used to drive the association procedures are usually based on "local" parameters like the received signal strength, and the base station/access point's load. In case connectivity is provided through a Wireless Mesh Network the quality perceived by the user upon association depends also on "global" network-wide parameters. This paper studies the dynamics of network association in Wireless Mesh Networks by resorting to game theoretic tools. We show how the association problem can be formalized as a non-cooperative game in which end users selfishly play to minimize a perceived association cost which accounts for characteristics of the entire path to reach the WMN gateway. The quality of the Nash Equilibria for the proposed game are then quantitatively analyzed, and preliminary numerical results on the perceived association cost are derived for sample network topologies.

Cognitive radio networks aim at enhancing spectrum utilization by allowing cognitive devices to opportunistically access vast portions of the spectrum. To reach such ambitious goal, cognitive terminals must be geared with enhanced spectrum management capabilities including the detection of unused spectrum holes (spectrum sensing), the characterization of available bands (spectrum decision), the coordination with other cognitive devices in the access phase (spectrum sharing), and the capability to handover towards other spectrum holes when licensed users kick in or if a better spectrum opportunity becomes available (spectrum mobility). In this paper, a game theoretic framework is proposed to evaluate spectrum management functionalities in cognitive radio networks. The spectrum selection process is cast as a non-cooperative game among secondary users who can opportunistically select the "best" spectrum opportunity, under the tight constraint not to harm primary licensed users. Different quality measures for the spectrum opportunities are considered and evaluated in the game framework, including the spectrum bandwidth, and the spectrum opportunity holding time. The cost of spectrum mobility is also accounted in the analytical framework. Numerical results are reported to assess the quality of the game equilibria.

Wireless sensor networks may support heterogeneous applications ranging from classical environmental monitoring, to more demanding integrated surveillance systems based on multimedia traffic. To this extent, we argue that the specific MAC layer solutions must be flexible enough to provide differentiated services based on the context of the specific WSN. In this work, we leverage the SPARE MAC protocol for WSN based on a receiver-oriented TDMA protocol, and we propose adaptive techniques to dynamically adjust the resource (slots) assignment which are able to track the changes in the network status (traffic increase/decrease, contention increase/decrease). We comment on the trade-off between performance gain and increased signalling complexity, by resorting to numerical results derived through simulation.

Cognitive Radio Networks (CRNs) are composed of frequency-agile radio devices that allow licensed (primary) and unlicensed (secondary) users to coexist, where secondary users opportunistically access channels without interfering with the operation of primary ones. From the perspective of secondary users, spectrum availability is a time varying network resource over which multi-hop end-to-end connections must be maintained. In this work, a theoretical outlook on the problem of routing secondary user flows in a CRN is provided. The investigation aims to characterize optimal sequences of routes over which a secondary flow is maintained. The optimality is defined according to a novel metric that considers the maintenance cost of a route as channels and/or links must be switched due to the primary user activity. Different from the traditional notion of route stability, the proposed approach considers subsequent path selections, as well. The problem is formulated as an integer programming optimization model and shown to be of polynomial time complexity in case of full knowledge of primary user activity. Properties of the problem are also formally introduced and leveraged to design a heuristic algorithm to solve the minimum maintenance cost routing problem when information on primary user activity is not complete. Numerical results are presented to assess the optimality gap of the heuristic routing algorithm.

A cross-layer communication protocol called VSN-module is introduced for wireless video sensor networks based on ultrawideband (UWB) radio technology at the physical layer. The core of our solution is a distributed path reservation scheme which routes video traffic from sources (sensors) to a sink. The path establishment takes into account the specific requirements of video flows such as bandwidth, end-to-end delay and jitter, as well as the current status of the traversed devices such as the available bandwidth, the energy consumption, and the quality of the wireless channels. Simulation experiments are carried out and the performance of the VSN-module is evaluated in terms of maximum end-to-end delivery delay, maximum delay jitter, and power consumption, under different video flow requirements.

We consider surveillance applications through wireless sensor networks (WSNs) with fully accessible areas to be monitored. In this context, the WSN topology can be planned a priori to maximize application efficiency. We propose an optimization framework for selecting the positions of wireless sensors to detect mobile targets traversing a given area. By leveraging the concept of exposure as a measure of coverage quality, we propose two problem versions: the minimization of the sensors installation cost while guaranteeing a minimum exposure, and the maximization of the exposure of the least exposed path subject to a budget on the sensors installation cost. We present compact mixed integer-linear programming formulations for these problems that can be solved to optimality for reasonable-sized network instances. Moreover, we develop a heuristic that is able to provide near-optimal solutions of the same instances in short computing time and also to tackle large size instances.

In future generation wireless access networks, the users will have the chance of choosing among multiple connectivity opportunities provided by different access networks (network selection problem). Moreover, the network operators themselves will have to implement effective resource allocation strategies taking wise decisions on the used technologies, frequencies, power levels, etc. This paper proposes a game-theoretic framework to model the problems of network selection and resource allocation, capturing the interdependencies of decisions taken by different players (users vs networks). Namely, we cast the problem as a non-cooperative game where users and access networks act selfishly according their specific objectives: maximization of the perceived quality of service for the end users, maximization of the number of customers for the access networks. We characterize the equilibria of the game by resorting to mathematical programming, and we derive numerical results to assess the "quality" of the equilibria.

This paper refers to a network scenario featuring a single RFID reader (interrogator) which aims at identifying a large number of objects geared with RFID tags, through multiple and sequential interrogations. Since several tags can answer to each interrogation, a problem of collisions arbitration at the reader arises. Classical previously proposed collisions arbitration protocols based on tree-search algorithms make use of channel feedback information on the outcome of the preceding transmissions either of binary type (collided/non collided), or ternary type (idle/successful/collided) achieving an average resolution time per object ranging between 2.1 and 2.8 interrogation cycles. In this paper, we show that the RFID transmission environment allows to obtain an enriched channel feedback information that can be leveraged to greatly improve the efficiency of the identification procedure. We further propose and analyze the arbitration protocol with advanced feedback (ARPA), able to asymptotically provide 100% efficiency. Finally, we show that the overhead needed to implement our protocol in practical RFID systems easily allows to achieve an average resolution time of about 1.4 interrogation cycles per object.

The choice of the transmission power levels adopted in Wireless Sensor Networks (WSNs) is critical to determine the performance of the network itself in terms of energy efficiency, connectivity and spatial reuse, since it has direct impact on the physical network topology. In this paper, a cooperative, lightweight and fully distributed approach is introduced to adaptively tune the transmission power of sensors in order to match local connectivity constraints. To accurately evaluate the topology control solution, a small-scale testbed based on MicaZ sensor nodes is deployed in indoor and outdoor scenarios. Practical measures on local connectivity, multi-hop connectivity, convergence time and emitted power are used to compare the proposed approach against previously proposed ones. Moreover, a simulation analysis complements the experimental one in large-scale WSN scenarios, where a testbed implementation becomes unfeasible.

The efficiency of point-to-point (PTP) connections in ad hoc networks highly depends on the capability of the medium access control (MAC) layer to reuse the shared wireless resource. One of the most critical impairments to the reuse capability is the exposed terminal problem which may prevent feasible PTP communications to run in parallel. In this paper we evaluate the impact of the exposed terminal on the reuse efficiency of PTP communications in ad hoc random networks. To this end, we propose a scheduling algorithm for PTP connections within the framework of ADHOC MAC, a recently proposed MAC scheme. The simulative results we gather show that the solution of the exposed terminal problem provides a reuse efficiency gain around the 40% in the network scenario we considered.

In this paper we propose SPARE MAC, a TDMA based medium access control (MAC) scheme for data diffusion in wireless sensor networks (WSNs). The rationale behind SPARE MAC is to spare energy through limiting the impact of idle listening and traffic overhearing. To this extent, SPARE MAC implements a distributed scheduling solution which assigns to each sensor specific radio resources (i.e., time slots) for reception, summarized as reception schedules (RS), and spreads the information of the assigned RS to neighboring sensors. A transmitting sensor can consequently become active in correspondence of the RS of its intended receiver only. We analyze the performance of SPARE MAC in terms of throughput, power consumption, and data delivery delay both through analytical models and through detailed simulations. Moreover, we compare the performance of SPARE MAC against SMAC.

Wireless mesh networks (WMNs) have raised great interest in both academy and industry thanks to their flexibility, low cost and easy deployment characteristics. MobiMESH is a WMN implementation that provides a complete framework for analyzing, studying and testing the behavior of a mesh network in a real-life environment. MobiMESH includes seamless mobility support for mesh clients, enhanced multi-radio-aware routing, channel assignment, as well as provisioning, management, monitoring and security platforms.

Wireless local area networks (WLANs) are spreading all over the planet with impressive speed and market penetration. They will replace traditional indoor wired local networks and allow flexible access outdoor, eventually competing with classical cellular systems (GSM, GPRS, UMTS, etc.) in the provision of wireless services. Although the small systems currently installed are planned using rules of thumb, their rapid spread and size increase requires quantitative methods to determine proper access points (AP) positioning. Previously proposed approaches to the coverage planning neglect the effect of the IEEE802.11 access mechanism, which limits system capacity when access points coverage areas overlap. Here we propose a new modelling approach that directly accounts system capacity and show that the resulting optimization problems of WLAN coverage planning can be seen as extensions of the classical set covering or maximum coverage problems. We present and discuss different formulations based on quadratic and hyperbolic objective functions and report some preliminary results on synthetic instances we generated.

Streaming video as a form of media is becoming increasingly popular on the Internet. Real-time media such as video requires delay constraints from the network to ensure good quality at the receiver. While watching a video stream on his portable device connected to the Internet through the last-hop wireless link, the mobile user of tomorrow will expect a good experience. But, the time-varying nature of the wireless link can cause video frames to be dropped/delayed, affecting the quality of video at the receiver. In this paper, we propose a link layer approach to improve the quality of MPEG video streaming over a wireless link. We use Bluetooth as the wireless technology on which to test our scheme. Our results show that the quality of streaming video can be substantially improved with our scheme, particularly in bad channel conditions.

Ad-hoc networks, though an attractive solution for many applications, still presents many unsolved issues, such as the hidden-terminal problem, flexible and prompt access, QoS provisioning, and efficient broadcast service. In this paper, we present a MAC architecture able to solve the above issues in environments with no power consumption limitation, such as networks for inter-vehicle communications. This new architecture is based on a completely distributed access technique, RR-ALOHA, capable to dynamically establish on a slotted/framed structure a reliable single-hop broadcast channel for each active terminal on the network. Though the proposed MAC uses a slotted channel, it can be adapted to operate on the physical layer of different standards, including the UMTS terrestrial radio access TDD, and the IEEE 802.11. The paper presents the mechanisms that compose the new MAC: the basic RR-ALOHA protocol, an efficient broadcast service and the reservation of point-to-point channels that exploit parallel transmissions. Some basic performance figures are discussed to prove the effectiveness of this protocol.

The provision of multimedia services to mobile users is one of the main goals of third generation (3G) systems. The traffic being transferred within 3G mobile networks will be composed by different information flows with various constraints on the required QoS (bit rate, delays, etc...). In order to reach such a goal, 3G standardization bodies have designed highly-flexible radio interfaces, characterized by a great number of physical parameters to be set by the operators. UMTS (Universal Mobile Telecommunication System) offers both circuit switched and packet switched transfer mode, and within each transfer mode, different QoS can be achieved by properly setting physical parameters such as the speed of physical channels, the power control scheme, the rate of the FEC protecting code, etc. We give an evaluation of the performance of W-CDMA UMTS radio access network (UTRA) when providing access to multimedia services. In particular, we analyze through detailed simulations a typical scenario where voice calls and Web-browsing sessions share the same frequency carrier, the former using the dedicated channels (DCH), the latter being transferred on the downlink shared channel (DSCH).

The provision of high speed packet data services in an efficient way is probably the most important challenge for UNITS since this can give an advantage with respect to second generation systems. The efficiency mainly depends on the radio interface and since it is characterized by a great flexibility it is of utmost importance to investigate the effect of different configurations on the system performance. In this paper we evaluate the performance of packet data services over downlink dedicated channels (DCH) by means of detailed simulations. Due to the traffic variability, ARQ mechanism and dosed loop power control the system behavior is quite complex and not so easily predictable as with constant rate services such as voice. We study the effect of several parameters (spreading factor, code rate, channel setup delay, etc.) on the system capacity by means of the delay-throughput curves. We show that the setting of parameters may be critical for system capacity and stability. For this reason we propose a flow control mechanism which is able to guarantee stability and to make the system capacity almost independent of channel rates.

The UMTS W-CDMA radio interface is characterized by great flexibility and a variety of different physical and logical channel types, rates and protections are possible, by choosing suitable parameters, such as spreading factors, code rates and ARQ schemes. In this paper we present the results, obtained by a detailed simulation, about the effect of several parameters and system alternatives on the capacity of the downlink shared channel.