In our increasingly tech-saturated world, from mobile apps and health sensors to autonomous cars and factory robots, we expect these devices to seamlessly integrate into our lives, enhancing safety and convenience. However, as these devices proliferate and their autonomy grows, ensuring they provide unobtrusive, yet effective support becomes crucial. The Horizon Europe KST multi-partner project “Distributed Multi-Sensor Systems for Human Safety and Health” (DistriMuSe) intends to support human health and safety by improved sensing of human presence, behaviour, and vital signs in a collaborative or common environment by means of multi-sensor systems, distributed processing and MachinelDeep Learning (ML/DL) techniques. In this paper, we focus on the DistriMuSe's approach on sports performance and health assessment, focusing on monitoring the physical activity of non-professional and hobby athletes, people who like sports and care about their health, elderly healthy people, and subjects affected by neurological disability (e.g., Parkinson's disease). The overall goal is to measure activity and exertion, estimating performance levels and determining maximum effort. We discuss the overall system-of-systems architecture, focusing on the adopted technologies.

The OPtimization of Electric Vehicle Autonomy (OPEVA) project enhances data aggregation for Electric Vehicles (EVs) by collecting critical real-time data (i.e., vehicle performance, battery health, charging behaviours) through heterogeneous data acquisition devices built on robust HW and integrated with Internet of Things (IoT) protocols. By combining internal sensor data and driver-specific behaviours with external information (e.g., road conditions, charging station availability), OPEVA maximizes vehicles performance, establishing secure and seamless data communication between EVs and the infrastructure, and using IoT and cloud computing tools alongside Vehicle-to-Everything (V2X) devices and networks. This paper focuses on the extensible data model ensuring semantic data integrity considering in- and out-vehicle factors, presenting data acquisition solutions dealing with OPEVA's semantic data model and their use in various Artificial Intelligence (AI)-powered use cases (e.g., range prediction, route optimization, battery management).

Over the past years, cryptocurrencies have experienced a surge in popularity within the financial markets. As of today, besides being considered for investment purposes, they also serve as a widely accepted form of currency for everyday transactions. Due to the intricate characteristics of financial markets and their dependence on various factors to determine the prices of stocks and assets, the ability to predict such prices is crucial to make investment choices, especially in terms of cryptocurrencies. In this work, a comparative analysis on the suitability of Deep Learning (DL) algorithms (effective for time series forecasting) in predicting the price of three cryptocurrencies (namely Bitcoin, BTC; Ethereum, ETH; and Ripple, XRP) is assessed in terms of both short-term and long-term prediction accuracy. The results, evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (denoted as \(R^{2}\) ), reveal that: Transformer is generally more effective for short-term forecasts and also performs well for long-term predictions; Convolutional Neural Network-Recurrent Neural Network (CNN-RNN) demonstrates the lowest complexity in terms of number of Multiply and ACcumulate (MAC) operations; SimpleRNN has the fewest parameters and the smallest FLASH memory requirement. Overall, CNN-Gated Recurrent Unit (CNN-GRU) provides the best joint accuracy-complexity for predicting BTC and ETH prices, whereas CNN-RNN yields superior results for XRP price prediction.

In Internet of Things (IoT)-like contexts, there is often the need to leverage traffic routing mechanisms among heterogeneous devices, especially when classical (and well-known) addressing paradigms cannot be adopted or supported by constrained IoT devices deployed on the field (e.g., due to memory footprint, internal limitations, etc.). This is even more true (and necessary) when nodes interact in unstructured networks (e.g., mesh-like) lacking a specific topology (e.g., exploiting flooding approaches to transfer information) and external “smart” devices should be allowed to interact with these networks. To this end, in this paper a multi-sink routing protocol, denoted as Routing on Mesh Bluetooth Low Energy (), is proposed. Our implementation relies on BLE advertisement channels and allows sink nodes to control topology formation and data collection (with both unicast and broadcast communications), with nodes identified with compressed addresses. A relevant experimental application to environmental lighting management is presented.

Localization services for precise and continuous monitoring of the locations of both humans and vehicles in industrial environments are among the most relevant applications in Industrial Internet of Things (IIoT) contexts, to maximize safety and optimize operational activities. Unfortunately, localization in industrial scenarios is particularly challenging because targets can generally move freely in both indoor and outdoor areas. In this paper, we propose a localization monitoring architecture based on a prototypical wearable IoT device equipped with Ultra-Wide Band (UWB), inertial, and GNSS/RTK technologies for seamless localization in heterogeneous environments. We focus on a Web of Things (WoT) approach, verifying suitability and limitations in a real use case scenario. Our approach shows that the proposed architecture can effectively enhance the safety of workers, detecting potentially dangerous events and triggering alarms (e.g., via smart buzzers or gas concentration warning devices) based on a cloud WoT architecture.

Nowadays, the need to efficiently process information in Internet of Things (IoT)-oriented heterogeneous scenarios has increased significantly, e.g., in all scenarios where unobtrusive environmental monitoring is beneficial for the involved people (e.g., inside public transport vehicles, indoor workplaces and offices, large public infrastructures, etc.). This objective typically requires the combination of heterogeneous IoT systems, which need to efficiently share information, e.g., through the Web of Things (WoT) paradigm. In this paper, we propose an edge computing-oriented flexible WoT architecture, with distributed intelligence, for air quality monitoring and prediction inside a public transport bus. Our results show that the proposed architecture allows seamless integration of heterogeneous IoT systems according to a WoT perspective, exploiting the device/edge/fog computing continuum and using containerized and secure processing modules.

Given the massive deployment of Internet of Things (IoT) applications over the last decade, the need for gateways able to efficiently route information flows across multiple heterogeneous networks has emerged, bringing new challenges. Therefore, the design and implementation of IoT gateways is crucial. In this paper, with reference to the architecture of a prototypical Multi-Interface Gateway (MIG) (based on Commercial-Off-The-Shelf, COTS, devices), we evaluate its performance: (i) analytically, through an innovative Markov chain-based model; (ii) by simulation, with a Python simulator; (iii) experimentally, through the (starting) COTS device-based prototype. In detail, the MIG is equipped with heterogeneous wireless communication interfaces (namely LoRaWAN, BLE, cellular 4G Cat. 4, and IEEE 802.11 Wi-Fi 2.4 GHz) and is applicable to multiple IoT scenarios. The obtained simulation and experimental results show the validity of the proposed analytical model. Further improvements of the proposed framework are eventually discussed.

In this paper, we analyze throughput and delay performance of clustered Machine Type Communication (MTC) devices which access an eNodeB utilizing a primary spectrum in underlay mode. We assume that the MTC devices form two clusters and there is an optimal preamble allocation between the two clusters to maximize the throughput. We further investigate the impact of the tolerable interference threshold on throughput, successful preamble decoding probability, and delay. Then, the impact of the preamble partition factor and the access barring factor on throughput and delay is analyzed. Finally, we evaluate the impact of the number of devices, retransmission requests, and preamble partitions on the delay.

In this work, a container-based architecture for the integration of Long Range Wide Area Network (LoRaWAN) end nodes—e.g., used to monitor industrial machines or mobile entities in specific environments—with Internet Protocol (IP)-based networks is proposed and its performance is investigated. To this end, we exploit the native service and resource discovery support of the Constrained Application Protocol (CoAP), as well as its light traffic requirements, owing to its use of User Datagram Protocol (UDP) rather than Transmission Control Protocol (TCP). This approach (i) adapts transparently (with no impact) to both private and public LoRaWAN networks, (ii) enables seamless interaction between LoRaWAN-based and CoAP-based nodes, through a logical “virtualization” of LoRaWAN nodes at server side, and (iii) enables routing among LoRaWAN end nodes, overcoming LoRaWAN's absence of inter-node communication and lack of compliance (at the end nodes' side) with IP. Two virtualization approaches are proposed: (i) virtualization of a single end node (represented as a CoAP server) per container and (ii) virtualization of multiple end nodes (as CoAP servers) per container. Finally, deployments of the proposed virtualization architectures, using both a laptop and an Internet of Things (IoT) device (e.g., a Raspberry Pi), are considered, highlighting how the best solution relies on the use of several containers, with more than one CoAP server per container.

In this paper, we propose an effective target localization strategy for Internet of Things (IoT) scenarios, where positioning is performed by resource-constrained devices. Target-anchor links may be impaired by Non-Line-Of-Sight (NLOS) communication conditions. In order to derive a feasible IoT-oriented positioning strategy, we rely on the acquisition, at the target, of a sequence of consecutive measurements of the Received Signal Strength Indicator (RSSI) of the wireless signals transmitted by the anchors. We then consider a pragmatic approach according to which the NLOS channels are pre-mitigated and “transformed” into equivalent Line-Of-Sight (LOS) channels to estimate more accurately each target-anchor distance. The estimated distances feed “agnostic” localization algorithms, operating as if all links were LOS. We experimentally assess the performance of our approach in indoor (IEEE 802.11-based) and outdoor (Long Term Evolution, LTE-based) scenarios, considering both geometric and Particle Swarm Optimization (PSO)-based localization algorithms. Even if NLOS mitigation per single communication link is very effective, our results show that, in a given environment, it is possible to derive an “average” NLOS mitigation strategy regardless of the specific position of the target in the given environment. This is crucial to limit the computational complexity at IoT nodes performing localization, yet guaranteeing a relatively high (for IoT scenarios) localization accuracy, especially in an IEEE 802.11-based indoor case (with six anchors). The obtained performance compares favorably (in relative terms) with that obtained with more sophisticated wireless technologies (e.g., Ultra-WideBand, UWB).

Bluetooth Low Energy (BLE) is a wireless communication technology for power-constrained Internet of Things (IoT) applications. BLE data can be transmitted via either the IPv6 or the Generic ATTribute (GATT) Profile protocol, with the former supporting dynamic IoT structures and the latter being application-friendly. In fact, GATT requires the data layout to be known in advance by peer devices, in order to properly interpret the received data. In this paper, we introduce DynGATT, a protocol that achieves the benefits of both IPv6 and GATT, by extending GATT in a seamless fashion to support dynamic IoT structures. The key idea of DynGATT is to use GATT descriptors, originally intended to specify data in static IoT scenarios, to also specify IoT systems whose structures may dynamically evolve. Peer devices reading these descriptors will know how to interpret the data of GATT characteristics provided by devices joining the IoT network. Because no additional data have to be transmitted, the connection time is then reduced with respect to classical BLE. DynGATT has been implemented and tested in an agricultural IoT application, with different types of sensor nodes. Our experimental evaluation shows that DynGATT is very power-efficient, despite its added flexibility. Its worst-case power consumption is only around 19.37 µA per data transmission and around 41.37 µA overall. This consumption can be further reduced by using the methods discussed in this paper. To the best of our knowledge, this work is the first to support dynamic IoT structures in a GATT-based setting.

In this paper, hybrid radio/inertial mobile target tracking for accurate and smooth path estimation is considered. The proposed tracking approach builds upon an Ultra WideBand (UWB)-based positioning algorithm, based on the Linear Hyperbolic Positioning System (LinHPS), with Time Difference of Arrival (TDoA) processing and anchors concentrated on a single hotspot at the center of the environment where the target moves. First, we design an Adaptive Radio-based Extended Kalman Filter (AREKF), which does not require a priori statistical knowledge of the noise in the target movement model and estimates the measurement noise covariance, at each sampling time, according to a proper LookUp Table (LUT). In order to improve the performance of AREKF, we incorporate inertial data collected from the target and propose three “hybrid” radio/inertial algorithms, denoted as Hybrid Inertial Measurement Unit (IMU)-aided Radio-based EKF (HIREKF), Hybrid Noisy Control EKF (HNCEKF), and Hybrid Control EKF (HCEKF). Our results on experimentally acquired paths show that the proposed algorithms achieve an average instantaneous position estimation error on the order of a few centimeters. Moreover, the minimum target path length estimation error, obtained with HCEKF, is on the order of 6% and 1% for two paths with lengths equal to approximately 17 m and 46 m, respectively.

The goal of this article is to investigate ultra wideband localization with time difference of arrival processing at the anchors. We consider scenarios where the anchors are placed very close to each other and the target to be localized is around the group of anchors. All target–anchor communications are assumed to be in line-of-sight. Since our analysis shows that symmetries in anchors’ placement, with respect to the target position, degrade the positioning accuracy of standard algorithms, we propose to use a subset selection strategy, where position estimates obtained with properly selected subsets of asymmetric anchors are fused together to get the final localization output. Our results show improved localization accuracy with respect to the use of all anchors, especially in estimating the angle of arrival. Finally, we analyze the impact of an inaccurate time synchronization among the anchors, deriving guidelines for hardware implementation.

Transmission latency is a key performance metrics in most wireless sensor network (WSN) applications. Nodes in a WSN often keep their radio transceivers off, and turn them on periodically using a duty cycling mechanism. The latter is a major source of delay in the network, because transmissions must wait for the next receiver wake-up. In this paper, we present a cross-layer approach to minimize latency of a request-response (RR) protocol adopted in an IEEE 802.15.4-based WSN where the IPv6 routing protocol for low-power and lossy networks (RPLs) is used. Extra wake-ups are generated dynamically to match the predicted arrival time of the response packet, in order to reduce the duty cycling delay. The proposed approach is verified with the Cooja simulator, relying on the Contiki operating system (OS). The observed experimental results show a shorter RR delay with respect to a phase alignment (PA) approach.

In this paper, we present an innovative inertial navigation system based on the data collected through the Inertial Measurement Unit (IMU) embedded in a commercial smart-phone. We propose an innovative step detection algorithm which is independent of the holding mode, the only assumption being that the device is hand-held (i.e., the user is texting/navigating or phoning) and its movement is related to the upper body displacement during walking. We also present a new approach able to automatically calibrate the step length estimation formula according to the smartphone positioning. The developed algorithms have been validated through a test campaign in which we have evaluated the system performance considering three different smartphone models and different path lengths. The obtained results show that the maximum step detection error is always below 4% (average: 2.08%; standard deviation: 1.82%) whereas the maximum path length estimation error is below 8.1% (average: 3.6%; standard deviation: 1.81%) in all the considered cases.

In this paper, we propose IoTChain, a combination of the OSCAR architecture [1] and the ACE authorization framework [2] to provide an E2E solution for the secure authorized access to IoT resources. IoTChain consists of two components, an authorization blockchain based on the ACE framework and the OSCAR object security model, extended with a group key scheme. The blockchain provides a flexible and trustless way to handle authorization while OSCAR uses the public ledger to set up multicast groups for authorized clients. To evaluate the feasibility of our architecture, we have implemented the authorization blockchain on top of a private Ethereum network. We report on several experiments that assess the performance of different architecture components.

Research efforts in the field of Internet of Things (IoT) are providing solutions in building new types of “network of networks,” going beyond the technological barriers due to intrinsic limitations of the constrained devices typically used in this context. Thanks to the improvement in communication/networking protocols and the hardware cost reduction, it is now possible to define new IoT architectures, combining the “micro” IoT paradigm, based on short-range radio technologies (e.g., IEEE 802.15.4 and IEEE 802.11), with the rising “macro” IoT paradigm, based on sub-GHz radio technologies. This allows the implementation of scalable network architectures, able to collect data coming from constrained devices and process them in order to provide useful services and applications to final consumers. In this paper, we focus on practical integration between micro and macro IoT approaches, providing architectural and performance details for a set of experimental tests carried out in the campus of the University of Parma. We then discuss challenges and solutions of the proposed micro-macro integrated IoT systems.

The Internet of Things (IoT) requires a compact naming scheme, which can also bring significant advantages to service registration and discovery. We propose a novel approach, denoted as distributed naming service, which provides a new naming scheme as well as an efficient service discovery protocol for wireless sensor networks. It is based on three pillars: 1) Bloom filters, to create compact names from node descriptions; 2) message propagation strategies, to publish and discover information-not only names-within the network; and 3) distributed caches, to store names within the network. In this paper, we assume ContikiMAC at layer 2, IPv6 and Routing Protocol for Low-Power and Lossy Networks (RPL) at layer 3, and we present two particular UDP-based message propagation strategies that take advantage of the RPL protocol at layer 3. We evaluate the performance of the proposed solutions through Contiki/Cooja simulations and on a real testbed, using the open and large scale FIT IoT-LAB.

In this paper, we investigate a wireless communication scenario, where polarization multiplexing is exploited to increase the spectral efficiency. Independent modems over each polarization are considered, with communication links affected by phase noise and cross-polarization interference (XPI). We devise a novel per-polarization soft decision-directed iterative receiver with separatea posterioriprobability-based synchronization and decoding. The synchronization algorithm relies on a minimum mean square error-based master–slave phase estimation followed by the cancellation of the XPI on the polarization of interest and requires no statistical knowledge of the phase noise process. The performance of the proposed iterative receiver is investigated for a pilot symbol-assisted low-density parity-check-coded quadrature amplitude modulation scheme.

Improving independent mobility in post-stroke patients is one of the main goals of most rehabilitation strategies. While quantitative gait assessment is crucial to provide a meaningful feedback on the recovery progress, the irregularity of hemiparetic walking prevents the use of classical Inertial Measurement Unit (IMU)-based gait analysis algorithms. In this paper, we propose a novel low-cost system, which relies on a single wearable IMU attached to the lower trunk, to estimate spatio-temporal gait parameters of both hemiparetic and healthy subjects. A new procedure for temporal features' computation and two modified versions of well-known step length (i.e., spatial features) estimators are derived. In both cases, we exploit dynamic calibration constants, related to the “power” of an individual gait pattern, to deal with the typical asymmetry and inter-subject variability of hemiparetic gait. The spatio-temporal features estimated with the proposed methods are compared with ground-truth parameters extracted by an optoelectronic system. The obtained results show very high correlations between estimated and reference values.

In this paper, we compare two novel algorithms for pedestrian navigation based on signals collected by a single wearable Magnetic, Angular Rate, and Gravity (MARG) sensor. The two navigation algorithms, denoted as Enhanced Pedestrian Dead Reckoning (EPDR) and De-Drifted Propagation (DDP), require the placement of the MARG sensor on the foot or on the chest of the test subject, respectively. Different methods for gait characterization are compared, evaluating navigation dynamics by using data collected through an extensive experimental campaign. The main goal of this research is to investigate the peculiarities of different inertial navigation algorithms, in order to highlight the impact of the sensor's placement, together with inertial sensor issues. Considering a closed path (i.e., ending at the starting point), the relative distance error between the starting point and the final estimated position is about 2% of the total travelled distance for both DDP and EPDR navigation algorithms. On the other hand, the error between the initial heading angle and the final estimated one is approximately 10° for EPDR and 7° for DDP, respectively.

This paper describes the development of a multifloor hybrid inertial/barometric navigation system. The prototype integrates several Magnetic Angular Rate and Gyroscope (MARG) sensors and a barometer. The inertial (MARG) sub-system, by properly processing the signals collected by the MARG sensors placed on the test subject's feet, reconstructs the two-dimensional navigation pattern by applying a Zero velocity UPdaTe (ZUPT) technique. Three different sensors' configurations are investigated in order to find the best performing set-up. A simpler configuration with a single MARG sensor is also considered to derive a reference performance benchmark without multiple MARG sensor fusion. The barometer, connected via usb to a Freakduino board, is used to detect the floor change. The fusion of inertial and barometric signals allows to fully reconstruct the movement of a person in both indoor and outdoor environments. The main goal of the proposed system is to allow accurate personal navigation without any external reference (i.e., radio signals, satellite signals, etc.). Considering a closed path, the relative distance error between the starting point and the final estimated position is below 2.5% of the total traveled distance.

In this paper, we analyze a cognitive wireless scenario, where a primary wireless network (PWN) coexists with a cognitive (or secondary) wireless network (CWN). The PWN uses licensed spectrum and the nodes of the CWN cooperate to detect idle subchannels (not used by the PWN's nodes), possibly taking into account the knowledge of their positions'. On the basis of this scenario, we present a simple, yet effective, framework to analyze the tradeoff between the CWN detection capabilities, i.e., the probability of detecting an unused lincensed subchannel, and the energy consumption needed to detect this subchannel. To this end, we introduce a novel performance indicator, denoted as detection energy efficiency. Our results show that there is an optimal working point, i.e., an optimal number of collaborating CWN nodes that allows to achieve the highest detection energy efficiency.

This study discusses synchronisation in phase noise‐impaired spectrally efficient communication systems employing high‐order modulations. In particular, an iterative receiver, where demodulation and decoding are separate from maximum a posteriori probability (MAP) synchronisation, is presented. The authors’ separate approach is tailored to the design of pragmatic iterative receiver schemes employing ‘off‐the‐shelf’ demodulation and decoding blocks. This allows full compatibility with already existing systems, which is attractive from the implementation viewpoint. The proposed MAP synchronisation algorithm also requires very limited knowledge of the phase noise process and achieves near coherent performance with moderate computational complexity. Although the approach is very general, the authors discuss its performance for low‐density parity‐check‐coded pilot symbol‐aided quadrature amplitude modulation schemes, demonstrating that a significantly lower computational complexity can be achieved with respect to benchmark joint receivers.

The analysis and assessment of motor tasks, such as gait, can provide important information on the progress of neurological disorders such as Parkinson's disease (PD). In this paper, we design a Boby Sensor Network (BSN)-based system for the characterization of gait in Parkinsonians through the extraction of kinematic features, in both time and frequency domains, embedding information on the status of the PD. The gait features extraction is performed on a set of 34 PD patients using a BSN formed by only three inertial nodes (one on the chest and one per thigh). We investigate also the relationship between the selected kinematic features and the Unified Parkinson's Disease Rating Scale (UPDRS) scores assigned to patients by expert neurologists. This work extends a previously proposed approach to the analysis of leg agility and sit-to-stand tasks and, as such, represents a further step to develop a system for automatic and comprehensive evaluation of different PD motor tasks. A performance analysis of different classification techniques is carried out, showing the feasibility of an automatic (and, eventually, remote) UPDRS scoring system, suitable for tele-health applications in the realm of affective medicine.

In this paper, we focus on a particular type of opportunistic ad-hoc networks, namely Pedestrian Ad-hoc NETworks (PANETs). In PANETs, nodes are densely distributed and each node may transmit information to all other nodes in the network via multi-hop broadcasting. Even though flooding is the simplest technique to broadcast information with multi-hop communications, it can be very inefficient because of redundant transmissions which may induce collisions. This problem is known, in the literature, as the “broadcast storm problem.” In this work, we present a novel probabilistic forwarding technique, denoted as Distance-based Silencing IF (DiSIF), which is derived from the probabilistic broadcasting protocol Irresponsible Forwarding (IF) and one of its extensions, denotes Silencing IF (SIF). The performance of the DiSIF protocol is analyzed and compared with those of other existing protocols, investigating the impact of fundamental network parameters. Lower bounds (exact and approximate) on the average number of hops, expedient to evaluate the propagation efficiency of DiSIF, are also derived. Finally, under the assumption that each node (e.g., a smartphone) relies on Global Positioning System (GPS) to estimate its position, the robustness of DiSIF against a GPS positioning error is investigated.

Recently, we have proposed a unified approach, based on the use of a Body Sensor Network (BSN) formed by a few body-worn wireless inertial nodes, for automatic assignment of Unified Parkinsons Disease Rating Scale (UPDRS) scores in the following tasks: Leg Agility (LA), Sit-to-Stand (S2S), and Gait (G). Unlike our previous works and the majority of the works appeared in the literature, where UPDRS tasks are investigated singularly, in the current paper we carry out a comparative investigation of the LA, S2S, and G tasks. In particular, we focus on the correlation between UPDRS values assigned to the three tasks by both an expert neurologist and our automatic system. We also consider an aggregate UPDRS score in order to highlight the relevance of each task in the assessment of the gravity of the Parkinson;s Disease (PD).

This paper focuses on phase noise-impaired communications. An efficient Maximum A-posteriori Probability (MAP) iterative synchronization algorithm, where detection and decoding are performed separately from phase estimation, is proposed. This approach has the following key advantages: (i) its computational complexity is relatively low and its performance is near optimal; (ii) it requires very limited statistical knowledge of the phase noise process; and (iii) it enables the direct use of “off-the-shelf” demodulation and decoding blocks. These features are particularly attractive from the implementation viewpoint, as they lead to the design of effective pragmatic high-order coded modulated schemes. The proposed iterative synchronization and decoding algorithm, evaluated for Low-Density Parity-Check (LDPC)-coded pilot symbol-assisted Quadrature Amplitude Modulation (QAM) schemes, entails a negligible energy efficiency loss with respect to optimized joint decoding and phase estimation approaches, with significantly lower computational complexity.

Summary The widespread and ubiquitous availability of Internet access enables the collective sharing of huge amount of data generated by heterogeneous sources. For example, the information, which will be exchanged among entities (sensors, people, and services) of future smart cities to enhance the security and lifestyle of their citizens, poses the challenging question of how this information can be efficiently and effectively maintained across the city. In this article, we propose a decentralized approach, based on the distributed geographic table (DGT) overlay scheme, which exploits geo‐referenced information about nodes to achieve efficient data management. After recalling DGT main concepts, we illustrate the possible node types and how information can be published and retrieved within the network. To cope with the unavoidable node failures and disconnections, our approach leverages upon randomized network coding to increase the robustness of publish/retrieval operations. Evaluation is carried out through an extensive simulation analysis for a realistic urban scenario using the metrics of efficiency in data publication/search, resource availability, and storage occupancy requirements. Results show the approach effectiveness for large‐scale sharing of geo‐referenced information and tradeoffs between redundancy overhead and resource availability. A few results obtained with a preliminary DGT implementation are also presented in the paper. Copyright © 2014 John Wiley & Sons, Ltd.

In this paper, by characterizing the leg agility (LA) task, which contributes to the evaluation of the degree of severity of the Parkinson's disease (PD), through kinematic variables (including the angular amplitude and speed of thighs' motion), we investigate the link between these variables and unified Parkinson's disease rating scale (UPDRS) scores. Our investigation relies on the use of a few body-worn wireless inertial nodes and represents a first step in the design of a portable system, amenable to be integrated in Internet of Things (IoT) scenarios, for automatic detection of the degree of severity (in terms of UPDRS score) of PD. The experimental investigation is carried out considering 24 PD patients.

In this study, we first characterize the sit-to-stand (S2S) task, which contributes to the evaluation of the degree of severity of the Parkinson's disease (PD), through kinematic features, which are then linked to the Unified Parkinson's disease rating scale (UPDRS) scores. We propose to use a single body-worn wireless inertial node placed on the chest of a patient. The experimental investigation is carried out considering 24 PD patients, comparing the obtained results directly with the kinematic characterization of the leg agility (LA) task performed by the same set of patients. We show that i) the S2S and LA tasks are rather unrelated and ii) the UPDRS distributions (for both S2S and LA tasks) across the patients have a direct impact on the observed system performance.

Recently, we have proposed a body-sensor-network-based approach, composed of a few body-worn wireless inertial nodes, for automatic assignment of Unified Parkinson's Disease Rating Scale (UPDRS) scores in the following tasks: Leg agility (LA), Sit-to-Stand (S2S), and Gait (G). Unlike our previous works and the majority of the published studies, where UPDRS tasks were the sole focus, in this paper, we carry out a comparative investigation of the LA, S2S, and G tasks. In particular, after providing an accurate description of the features identified for the kinematic characterization of the three tasks, we comment on the correlation between the most relevant kinematic parameters and the UPDRS scoring. We analyzed the performance achieved by the automatic UPDRS scoring system and compared the estimated UPDRS evaluation with the one performed by neurologists, showing that the proposed system compares favorably with typical interrater variability. We then investigated the correlations between the UPDRS scores assigned to the various tasks by both the neurologists and the automatic system. The results, based on a limited number of subjects with Parkinson's disease (PD) (34 patients, 47 clinical trials), show poor-to-moderate correlations between the UPDRS scores of different tasks, highlighting that the patients' motor performance may vary significantly from one task to another, since different tasks relate to different aspects of the disease. An aggregate UPDRS score is also considered as a concise parameter, which can provide additional information on the overall level of the motor impairments of a Parkinson's patient. Finally, we discuss a possible implementation of a practical e-health application for the remote monitoring of PD patients.

In this paper, by characterizing the Leg Agility (LA) task, which contributes to the evaluation of the degree of severity of the Parkinson's Disease (PD), through kinematic variables (including the angular amplitude and speed of thighs' motion), we investigate the link between these variables and Unified Parkinson's Disease Rating Scale (UPDRS) scores. Our investigation relies on the use of a few body-worn wireless inertial nodes and represents a first step in the design of a portable system for automatic detection of the degree of severity (in terms of UPDRS score) of PD. The experimental investigation is carried out considering 24 PD patients.

The Internet of Things requires a naming service that can also be beneficial for searching for services and applications. To avoid the traditional DNS approach in which a DNS server provides mapping of names to IP addresses in its domain, we propose a novel network service called DINAS (DIstributed NAming Service). It is based on three pillars: 1) Bloom filters for creating compact names from node descriptions, 2) distributed caches for storing names within the network, and 3) overlay routing strategies to publish and discover information - not only names - within the network. In this work, we assume ContikiMAC at Layer 2, 6LoWPAN at Layer 2.5, IPv6 and RPL (routing protocol) at Layer 3, and we present a particular UDP-based overlay routing strategy. We evaluate the proposal by Cooja simulations and compare its performance with a centralized naming service at the sink. The results show that DINAS outperforms the centralized solution.

Wireless Sensor Networks (WSNs) consist of a collection of spatially distributed radio transceivers with attached sensors that can measure and gather information from the environment. In this paper, we focus on the application of WSNs to indoor localization and, for this purpose, we propose the use of a Ultra Wide Band (UWB) WSN. The use of UWB signals guarantees robust performance in dense multipath environments, making them an attractive choice for indoor localization. In this paper, we discuss on different localization strategies: first classic geometric approaches are considered, then the mathematical framework is re-interpreted as an optimization problem. In the latter context, we propose the use of Particle Swarm Optimization (PSO) in particular, which can overcome limitations of classic (geometric) approaches.

In Wireless Sensor Networks (WSNs) for monitoring applications, energy saving and fast data collection are two challenging tasks. Asynchronous radio duty cycling protocols can achieve very low energy consumption in low traffic conditions and they are fault-tolerant to clock drifts. However, they may exhibit a delay degradation due to the decoupled wake-up periods of the nodes. In this paper, we present RAWMAC, a cross-layer approach where RPL, a tree-based routing protocol, orchestrates the asynchronous duty-cycled ContikiMAC MAC layer. The wake-up instants of the nodes are dynamically aligned, with respect to the RPL topology, to minimize the delay for data collection. We implement RAWMAC for the Contiki operating system and we analyze the impact of several key system parameters. Results show that RAWMAC outperforms ContikiMAC in terms of delay for data collection, while keeping the same performance in terms of throughput and energy consumption.

The Internet of Things (IoT) aims at connecting billions of devices in an Internet-like structure. This gigantic information exchange enables new opportunities and new forms of interactions among things and people. A crucial enabler of robust applications and easy smart objects' deployment is the availability of mechanisms that minimize (ideally, cancel) the need for external human intervention for configuration and maintenance of deployed objects. These mechanisms must also be scalable, since the number of deployed objects is expected to constantly grow in the next years. In this work, we propose a scalable and self-configuring peer-to-peer (P2P)-based architecture for large-scale IoT networks, aiming at providing automated service and resource discovery mechanisms, which require no human intervention for their configuration. In particular, we focus on both local and global service discovery (SD), showing how the proposed architecture allows the local and global mechanisms to successfully interact, while keeping their mutual independence (from an operational viewpoint). The effectiveness of the proposed architecture is confirmed by experimental results obtained through a real-world deployment.

In this paper, we consider orthogonal multiple access coding schemes, where correlated sources are encoded in a distributed fashion and transmitted through additive white Gaussian noise (AWGN) channels to an access point (AP). At the AP, component decoders, which are associated with the source encoders, iteratively exchange soft information by taking into account the source correlation. The first goal of this paper is to investigate the ultimate achievable performance limits in terms of a multi-dimensional feasible region in the space of channel parameters, deriving insights on the impact of the number of sources. The second goal is the design of pragmatic schemes, where the sources use “off-the-shelf” channel codes. In order to analyze the performance of given coding schemes, we propose an extrinsic information transfer-based approach, which allows to determine the corresponding multi-dimensional feasible regions. On the basis of the proposed analytical framework, the performance of pragmatic coded schemes, based on serially concatenated convolutional codes, is discussed.

In this paper, we focus on the characterization of the Leg Agility (LA) task, which contributes to the evaluation of the degree of severity of the Parkinson's Disease (PD) through semiquantitative evaluation scales, such as the Unified Parkinson's Disease Rating Scale (UPDRS). By extracting relevant kinematic variables, such as the angular amplitude and speed of thighs' motion, we analyze, in a comparative way, the results obtained when a healthy subject and a PD patient perform the LA task. Our investigation relies on the use of wireless inertial systems, whose accuracy is confirmed by direct comparison with optoelectronic systems. Although preliminary, the proposed analysis allows to derive significant insights in possible approaches to accurately evaluate the degree of severity of PD.

In scenarios like the surveillance of isolated areas, when the border node of a network does not have a permanent connection with the Internet, Wireless Sensor Networks (WSNs) are calling for resilient in-network data storage techniques which minimize the risk of data loss. The efficiency of these techniques can be largely improved exploiting information on the status of the network, such as that used by routing protocols. In particular, one of the most used protocol in Internet of Things (IoT) scenarios is the IPv6 Routing Protocol for Low power and lossy networks (RPL). In this paper, we propose a redundant distributed data storage and retrieval mechanism to increase the resilience and storage capacity of a RPL-based WSN against local memory shortage. We evaluate our approach in the Contiki operating system through extensive analysis with the Cooja simulator.

The Routing Protocol for Low power and Lossy Networks (RPL) is the IETF standard for IPv6 routing in low-power wireless sensor networks. It is a distance vector routing protocol that builds a Destination Oriented Directed Acyclic Graph (DODAG) rooted towards one sink (the DAG root), using an objective function and a set of metrics/constraints to compute the best path. In this paper, we propose a routing metric which minimizes the delay towards the DAG root, assuming that nodes run with very low duty cycles (e.g., under 1%) at the MAC layer. We evaluate the proposed routing metric with the Contiki operating system and compare its performance with that of the Expected Transmission Count (ETX) metric. Moreover, we propose some extensions to the ContikiMAC radio duty cycling protocol to support different sleeping periods of the nodes.

In this paper, we consider a particular type of wireless ad-hoc networks, namely Pedestrian Ad-hoc NETworks (PANETs), where all nodes in the network are sources and destinations of information. In particular, we refer to an application where nodes report their presence by broadcasting an hello message to all other nodes in both IEEE 802.11 monodimensional and bidimensional scenarios. The system performance, in terms of throughput and delay, is analyzed using a recently proposed broadcasting protocol, namely Irresponsible Forwarding (IF) and its extension, denoted as Silencing IF (SIF). The proposed framework allows to determine a critical hello generation rate below which multi-source broadcasting is efficient.

In this paper, we consider the problem of locating an Automated Guided Vehicle (AGV) which moves on a plane in an industrial environment by means of Ultra-Wide Band (UWB) signaling from fixed Anchors Nodes (ANs) situated in the (three-dimensional) space. An analytical approach to optimize, under proper (realistic) constraints, the placement of the ANs used to locate the AGV is proposed. Analytical results are confirmed by simulations.

In this paper, the performance of a cooperative spectrum sensing (CSS) is evaluated, considering maximal ratio combining (MRC) fusion in the presence of Rayleigh fading. A cognitive radio (CR) senses the primary users (PUs) using an energy detector (ED) in the sensing channel (S-channel). If a CR is selected to transmit its local decisions to a fusion centre (FC), it does so using a reporting (R)-channel with binary phase shift keying (BPSK) signaling. In the present work, we consider censoring of CRs based on a characterization of Rayleigh faded R-channels. In other words, CRs, whose estimated R-channel fading coefficients towards the FC exceed a predefined threshold (censoring threshold), are allowed to transmit. MRC fusion is considered at the FC to estimate the performance in terms of average missed detection and total error probabilities for various values of (i) the censoring threshold, (ii) the number of CRs, (iii) the average S-channel and R-channel signal-to-noise ratios (SNRs), under both perfect and imperfect channel estimations. The performance of CSS system with MRC fusion is also compared, for various values of the network parameters, with the performance with majority logic fusion.

Recent years have seen the relentless market explosion of mobile devices, whose ever increasing capabilities (in terms of computational power, networking, and sensing) make them attractive to an endless number of connected applications and services (especially in business and infotainment domains) which can be fully experienced in mobility. This huge market growth naturally involves a constant increase of mobile internet accesses with a consequent overload for mobile operators and potentially a reduced performance for mobile users. In this scenario and during last years, the research field of data offloading and Vertical HandOver (VHO) has gained a significant attention by service providers to start offloading mobile data traffic from 3G/4G networks to WiFi networks. The reduction of the load on cellular network is instrumental to allow the user to be Always Best Connected (ABC) with limited costs. In this paper, we present and analyze the performance of a real VHO-enabled ABC mobile application for Android Platform. The application has been tested in a national trial involving several users all over Italy, commercial (Guglielmo Srl) and private WiFi networks and cellular networks of the main Italian mobile operators for more than a month and 150.000 distinct logs collected during the evaluation.

Many applications for ad-hoc networks are based on a multicast communication paradigm, where a single source sends common data to many receivers. In these contexts, it is possible to efficiently secure the multicast communications by leveraging on a common secret key, denoted as “group key”, shared by multiple users. In this paper, we propose a novel centralized approach that efficiently addresses the problem of deriving and managing a group key in generic ad-hoc networks and Internet of Things (IoT) scenarios, reducing the computation overhead due to group membership changes caused by user's joins and leaves. In particular, the proposed method takes advantage of the assumption of two possible leave strategies: (i) at a pre-determined time interval selected when the member joins the group or (ii) at any unpredictable time interval, as in the case of membership revocation.

In this paper, some novel results on the encoding complexity of network coding and its relation with the network topology are reported. The encoding complexity in network coding is defined as the number of nodes which have to perform coding operations in order to achieve the multicast capacity. These nodes are referred to as coding points. Known results state that the number of coding points is cubic in the mincut and quadratic in the number of receivers. In this paper, we show, through extensive simulations and through analysis of these results, that the number of coding points tends to increase linearly in the min-cut and the number of receivers in random graphs. We show that this is correlated to the length of path from the source to the receivers. To verify this, we also analyze pseudo-random graphs with a larger path length.

In this paper, we present a novel approach to early diagnosis, through a video processing-based approach, of the presence of neonatal seizures. In particular, image processing and gesture recognition techniques are first used to characterize typical gestures of neonatal seizures. More precisely, gesture trajectories are characterized by extracting some relevant features. In particular, selecting the point with the maximum amplitude of the optical flow vector of the video frame sequence, during a newborn movement, is selected and then tracked through an algorithm based on template matching and optical flow. The observed features are then clustered using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The proposed approach allows to efficiently differentiate pathological repetitive movements (e.g., clonic and subtle seizures) from random ones.

In this paper, we consider a novel low-complexity real-time image-processing-based approach to the detection of neonatal clonic seizures. Our approach is based on the extraction, from a video of a newborn, of an average luminance signal representative of the body movements. Since clonic seizures are characterized by periodic movements of parts of the body (e.g., the limbs), by evaluating the periodicity of the extracted average luminance signal it is possible to detect the presence of a clonic seizure. The periodicity is investigated, through a hybrid autocorrelation-Yin estimation technique, on a per-window basis, where a time window is defined as a sequence of consecutive video frames. While processing is first carried out on a single window basis, we extend our approach to interlaced windows. The performance of the proposed detection algorithm is investigated, in terms of sensitivity and specificity, through receiver operating characteristic curves, considering video recordings of newborns affected by neonatal seizures.

In this paper, we consider the problem of fusing decisions in a distributed detection system when the local binary decisions made at the sensors, relative to observations of a common binary phenomenon, are transmitted over wireless links subject to Rayleigh flat fading and additive noise. A training-based channel estimator is used at the fusion center (FC) to estimate the complex Gaussian fading coefficients characterizing the channels between the sensors and the FC. We use channel state information (CSI) on the fading coefficients for censoring the sensors. Locally optimal decision threshold is considered for binary quantization at the sensors. The detection error probability, using a maximal ratio combining (MRC) fusion rule, is selected as a qualitative measure of system performance and is evaluated by means of simulations. We also use majority logic fusion at the FC to compare detection error probability with MRC after censoring sensors. We study the effects, on the system performance, of the channel estimation error, the channel signal-to-noise ratio (SNR), the sensor SNR, and the number of selected sensors.

In this paper, we present a mathematical framework to study decentralized detection in IEEE 802.15.4-compliant clustered wireless sensor networks (WSNs). Sensors are organized in clusters, with fusion centers (FCs) acting as cluster heads, and are supposed to observe the same common binary phenomenon. The FCs periodically send queries to sensors and wait for replies. Once data are received, the FCs perform majority fusion. Decisions are sent to an access point (AP), where a final data fusion is carried out and an estimate of the phenomenon is obtained. A data aggregation strategy at sensors is also investigated. The goal of this paper is to jointly investigate decentralized detection, medium access control (MAC), and data aggregation issues. Our results show that an optimum WSN clustering configuration exists and it depends on the data aggregation strategy used and the MAC parameters.

This paper presents a novel approach to the extraction of video features for real-time detection of neonatal seizures. In particular, after identification of a proper Region Of Interest (ROI) within the video frame, the broadening factor and the maximum distance between consecutive pairs of zeros of a properly extracted average differential luminosity signal are shown to be relevant features for a diagnosis. The ROI is selected by defining an area around the point where the maximum amplitude of the optical flow vector of that video frame sequence is observed. The located point is then tracked by an algorithm based on template matching and optical flow. The proposed approach allows to differentiate pathological movements (e.g., clonic and myoclonic seizures) from random ones.

In this paper, we study the performance of non-cooperative multiple access systems with noisy separated channels, where correlated sources communicate to an access point (AP) through block-faded links. In the considered scenario, perfect channel state information (CSI) is assumed at the receiver while no CSI is available at the transmitters. We first consider uncoded transmissions from the sources to the AP, which exploits the source correlation to carry out joint channel detection (JCD). In this scenario, we propose an analytical approach to evaluate the achievable performance in terms of average bit error rate (BER). We then investigate the impact of coding, considering the same fixed coding scheme at each source. Then, we consider an extrinsic information transfer (EXIT) chart-based framework to optimize the design of concatenated and low-density parity-check (LDPC) codes for JCD schemes.

In this paper, we focus on the design of optimized binary convolutional codes (CCs) and serially concatenated convolutional codes (SCCCs) in the presence of a-priori information (API) at the receiver. For large signal-to-noise ratios (SNRs), we first propose a CC design criterion based on the minimization of a union bound on the bit error probability (BEP). In this case, relevant performance gains, with respect to previously proposed CCs, are obtained. These gains persist even in the presence of estimation errors on the API. Then, we apply the same union bound-based design criterion to SCCCs. Since the BEP of SCCCs is characterized by a typical waterfall shape, the proposed union bound-based design criterion is accurate only at large SNR, to estimate the BEP floor. In order to complement this analysis, we propose a density evolution-based approach to optimize the SCCC design in terms of minimization of the SNR of the "knee" of the BEP curve. The obtained simulation results show substantial gains with respect to previously proposed parallel concatenated convolutional coding (PCCCing) schemes optimized under the assumption of no API at the decoder. Moreover, in the presence of strong API the proposed SCCCs allow to approach the Shannon limit (SL) more than any previously proposed turbo coding scheme.

This paper addresses the problem of engineering energy-efficient target detection applications using unattended Wireless Sensor Networks (WSNs) for long-lasting surveillance of areas of interest. As battery energy depletion is an issue in this context, an approach consists of switching on and off sensing and communication modules of wireless sensors according to duty cycles. Making these modules work in an intermittent fashion impacts (i) the latency of notification transmission (depending on the communication duty cycle) and (ii) the probability of missed target detection (depending on the number of deployed nodes and the sensing duty cycle). In order to optimize the system parameters according to performance objectives, we first derive an analytical engineering toolkit which evaluates the probability of missed detection (Pmd), the notification transmission latency (D), and the network lifetime (¿) under the assumption of random node deployment. Then, we show how this toolbox can be used to optimally configure system parameters under realistic performance constraints.

In a self-organizing vehicular network, vehicles share and distribute information by rebroadcasting a received information packet to their neighbors. An efficient broadcast technique can offer a high reactivity without sacrificing the communication reliability. Therefore, broadcast techniques are particularly suitable for safety-related vehicular transmissions, whose goal is reaching reliably the widest area in the shortest time. Among the numerous solutions appeared in the literature, the probabilistic broadcast approaches seem to be promising and not yet accurately analyzed. Since the interaction between a high level broadcasting protocol with the lower layers cannot be ignored, in this work we analyze the behavior of a recently proposed broadcast technique, denoted as the Irresponsible Forwarding (IF), in IEEE 802.11 networks. Our attention concentrates on the Medium Access Control (MAC) layer, which is affected by some critical impairments for broadcasting, such as the hidden terminal problem and self-interference. In this work, we evaluate the benefits brought by the use of IF to perform efficient broadcasting in IEEE 802.11 networks.

In this paper, we present a novel pragmatic approach, referred to as detection by multiple trellises, to perform trellis-based detection over realistic channels. More precisely, we consider channels with unknown parameters and apply the concept of detection by multiple trellises to forward-backward (FB) algorithms. The key idea of our approach consists, first, of properly quantizing the channel parameters and, then, considering replication of coherent FB algorithms operating on parallel trellises, one per hypothetical quantized value. In order to make the receiver robust against a possibly time-varying channel parameters, the proposed soft-output algorithms perform a proper "manipulation" of the forward and backward metrics computed by the parallel FB algorithms at regularly spaced trellis steps. We consider two significant examples of application: detection over (i) phase-uncertain channels and (ii) fading channels. The performance of the proposed algorithms is investigated considering differentially encoded (DE) quaternary phase shift keying (QPSK) and iterative detection schemes based on low-density parity-check (LDPC) codes. Besides having a low complexity, the proposed soft-output algorithms turn out to be robust, flexible, blind, in the sense that no knowledge of the channel parameter statistics is required, and highly parallelizable, as it is desirable in high-throughput future wireless communication systems.

In this paper, we investigate the ultimate performance limits, in terms of achievable information rate (IR), of communication systems impaired by impulse noise. We compare single carrier (SC) and multi-carrier (MC) transmission systems employing quadrature amplitude modulation (QAM) formats. More precisely, we consider SC schemes with coded modulations and MC systems based on orthogonal frequency division modulation (OFDM). For the MC schemes, we introduce a theoretically equivalent channel model which makes the computation of the IR feasible. This simple channel model will be referred to as interleaved MC. We show that, in the presence of impulse noise and except for systems operating at very high spectral efficiency, the IR of MC schemes is lower than that of SC schemes. More precisely, use of MC schemes may lead to an unavoidable fundamental loss with respect to SC schemes at typical coding rates, whereas MC schemes are to be preferred for very high coding rates or in uncoded systems. These results hold for additive white Gaussian noise (AWGN) and dispersive channels, either considering plain OFDM or MC schemes employing water-filling and bit-loading algorithms. In order to validate our theoretical results, we also obtain the bit error rate (BER) performance of SC and MC schemes through Monte Carlo simulations. A few trellis-coded modulation (TCM) and low-density parity-check (LDPC)-coded schemes are considered. The obtained SNR loss in the BER curves between the AWGN and impulse noise channels matches well with the corresponding IR gap.

Routing in ad hoc wireless networks does not simply consist in finding a route with shortest length (as in wired networks with virtually error-free communication links), but it requires the creation of a stable and good quality communication route to avoid any unnecessary packet loss. In this paper,we discuss physical layer-oriented routing in ad hoc wireless networks, and we analyse the potential advantages of combining the use of power control (PC) with the chosen routing strategy. More precisely, we propose a modified ad hoc on-demand distance vector (MAODV) routing protocol, with and without PC, derived from the AODV-routing protocol by considering the bit error rate at the end of a multi-hop path as the metric to be minimised for route selection. In other words, we consider routing with a physical layer-oriented quality of service criterion, and we analyse the system performance in scenarios with either strong line-of-sight (LOS) or shadowed communications. Although in a scenario with strong LOS communications there are a few cases where the MAODV-PC protocol offers the best performance, in the presence of shadowed communications the proposed physical layer-oriented strategy is not attractive.

In this paper, we present an overview on the design of algorithms for iterative detection over channels with memory. The starting point for all the algorithms is the implementation of soft-input soft-ouput maximum a posteriori (MAP) symbol detection strategies for transmissions over channels encompassing unknown parameters, either stochastic or deterministic. The proposed solutions represent effective ways to reach this goal. The described algorithms are grouped into three categories: i) we first introduce algorithms for adaptive iterative detection, where the unknown channel parameters are explicitly estimated; ii) then, we consider finite-memory iterative detection algorithms, based on ad hoc truncation of the channel memory and often interpretable as based on an implicit estimation of the channel parameters; and iii) finally, we present a general detection-theoretic approach to derive optimal detection algorithms with polynomial complexity. A few illustrative numerical results are also presented.

This paper investigates whether and when route reservation-based (RB) communication can yield better delay performance than non-reservation-based (NRB) communication in ad hoc wireless networks. In addition to posing this fundamental question, the requirements (in terms of route discovery, medium access control (MAC) protocol, and pipelining, etc.) for making RB switching superior to NRB switching are also identified. A novel analytical framework is developed and the network performance under both RB and NRB schemes is quantified. It is shown that if the aforementioned requirements are met, then RB schemes can indeed yield better delay performance than NRB schemes. This advantage, however, comes at the expense of lower throughput and goodput compared to NRB schemes

In this paper, we analyze the lifetime of clustered sensor networks with decentralized binary detection under a physical layer quality of service (QoS) constraint, given by the maximum tolerable probability of decision error at the access point (AP). In order to properly model the network behavior, we consider four different distributions (exponential, uniform, Rayleigh, and lognormal) for the single sensors' lifetime. We show the benefits, in terms of longer network lifetime, of adaptive reclustering. On the other hand, absence of reclustering leads to a shorter network lifetime, and we show the impact of various clustering configurations under different QoS conditions. Our results show that the organization of sensors in a few big clusters is the winning strategy to maximize the network lifetime

The remarkable surge in research on ad hoc wireless networks is largely due to their potential in offering insfrastructureless communications. While initially studied adopting a "conventional" networking approach, based upon years of research experience on wired computer networks (with virtually error-free communication links), the presence of unreliable wireless communication links necessitates a communication-theoretic foundation for the design and analysis of wireless ad hoc networks. Such a novel comprehensive "bottom-up" perspective was, for the first time, presented in OK Tonguz et al. (2006). In this paper, we summarize the communication-theoretic framework, where the impact of physical layer on the network performance and its interaction with higher layers is taken into account. The main goal of this paper is to provide the reader with the intuition behind the comprehensive approach, rather than the mathematical details of the approach

In this letter, we discuss the performance of low-density parity-check (LDPC) codes on memoryless channels. Using a recently proposed analysis technique based on extrinsic information transfer (EXIT) charts, we present an interpretation of the known fact that the bit-error rate (BER) performance of an ensemble of LDPC codes shows little dependence on the specific memoryless channel. This result has been partially observed in the literature for symmetric channels and is here extended to asymmetric channels. We conjecture and demonstrate that the performance of an ensemble of LDPC codes depends primarily and solely on the mutual information (MI) between the input and the output of the channel. As a validation of this conjecture, we compare the performance of a few LDPC codes with various rates for five representative memoryless (both symmetric and asymmetric) channels, obtaining results in excellent agreement with the EXIT chart-based prediction

Power conservation is one of the most important issues in wireless ad hoc and sensor networks, where nodes are likely to rely on limited battery power. Transmitting at unnecessarily high power not only reduces the lifetime of the nodes and the network, but also introduces excessive interference. It is in the network designer's best interest to have each node transmit at the lowest possible power while preserving network connectivity. In this paper, we investigate the optimal common transmit power, defined as the minimum transmit power used by all nodes necessary to guarantee network connectivity. This is desirable in sensor networks where nodes are relatively simple and it is difficult to modify the transmit power after deployment. The optimal transmit power derived in this paper is subject to the specific routing and medium access control (MAC) protocols considered; however, the approach can be extended to other routing and MAC protocols as well. In deriving the optimal transmit power, we distinguish ourselves from a conventional graph-theoretic approach by taking realistic physical layer characteristics into consideration. In fact, connectivity in this paper is defined in terms of a quality of service (QoS) constraint given by the maximum tolerable bit error rate (BER) at the end of a multihop route with an average number of hops

In this paper, we consider the asymptotic (for high signal-to-noise ratio, SNR) behavior of the mutual information (MI) between the input and the output of a two-dimensional additive white Gaussian noise (AWGN) channel with continuous input distributions. In particular, the obtained results apply to modulation formats with dense constellations. The accuracy of the presented results is the higher the denser are the considered constellations. We find two simple expressions relating the MI of the considered continuous input distribution, as a function of the SNR, to that of the Gaussian input distribution, in one- and two-dimensional cases, respectively.

In this paper, we compare the performance of two possible switching techniques for ad hoc wireless networks: namely, reservation-based (RB) and non-reservation-based (NRB) switching. Fundamental network performance metrics, such as throughput and delay, are studied to understand the tradeoffs between the two switching schemes. In particular, we identify the circumstances where each of these two schemes is better than the other.

Power conservation is one of the most important issues for ad hoc wireless networks where nodes are likely to rely on limited battery power. Transmitting at unnecessarily high power not only reduces the lifetime of the nodes and the network, but also introduces excessive interference. It is in the network designer's best interest to have each node transmit at the lowest possible power while preserving network connectivity. In this paper, we investigate the optimal common transmit power, defined as the minimum transmit power used by all nodes necessary to guarantee network connectivity. In particular, we show that for a given route BER and node spatial density, there exists a global optimal data rate at which the transmit power can be globally minimized. Moreover, we also show that there exists a critical node spatial density at which the optimal transmit power is the minimum possible for a given data rate and a given route BER.

In this paper, we present a novel class of simple soft-input soft-output (SISO) detection algorithms for transmission over phase-uncertain channels. In order to limit the complexity, the set of possible phase values (i.e., the interval [0,2pi)) is properly quantized. We then use a forward-backward (FB) algorithm in correspondence to each quantized phase value. The forward and backward metrics computed by the different FB algorithms are properly combined to make the receiver robust against the time-varying phase. We apply the proposed SISO algorithms to perform joint detection and decoding of serially concatenated low-density parity-check (LDPC) codes and differentially encoded (DE) quaternary phase shift keying (QPSK), transmitted over a phase-uncertain channel. As a validation of the effectiveness of the proposed algorithms, we show that the performance loss with respect to that of the corresponding ideal (i.e., perfectly coherent) system is limited. Besides having a limited complexity, the proposed SISO algorithms are blind, i.e., no knowledge of the channel phase statistics is required, and highly parallelizable, as desirable in high-throughput future wireless communication systems

In this paper, we investigate the impact of the channel utilization ratio (CUR) on the performance of ad hoc wireless networks. Given that a node can hold a multi-hop route for a time interval defined as the reserved channel utilization interval (RCUI), we assume that the node effectively utilizes the reserved route for an interval defined as the effective channel utilization interval (ECUI), the duration of which corresponds to the duration of the message to be transmitted. This models a realistic scenario, where a node may use the shared radio medium for only a portion of the reservation interval. Defining the CUR as the ratio between the RCUI duration and the ECUI duration, we develop an analytical framework which leads to the evaluation of the "optimal" CUR for the maximization of the effective transport capacity, a concept recently introduced by the authors and representing the actual bandwidth-distance product carried by the network.

In this paper, the performance of two possible switching schemes for ad hoc wireless networks, namely reservation-based (RB) and non-reservation-based (NRB) is investigated. It is shown that route reservation would be the scheme of choice in a scenario where delay is a constraint, the network is dense, and/or nodes move with moderate speed. However, a non-reservation scheme is preferable in a scenario where the network is sparse, there is heavy traffic load, nodes are static, and/or delay is not a major concern.

In this paper, we present a general approach to finite-memory detection. From a semi-tutorial perspective, a number of previous results are rederived and new insights are gained within a unified framework. A probabilistic derivation of the well-known Viterbi algorithm, forward-backward, and sum-product algorithms, shows that a basic metric emerges naturally under very general causality and finite-memory conditions. This result implies that detection solutions based on one algorithm can be systematically extended to other algorithms. For stochastic channels described by a suitable parametric model, a conditional Markov property is shown to imply this finite-memory condition. This conditional Markov property, although seldom met exactly in practice, is shown to represent a reasonable and useful approximation in all considered cases. We consider, as examples, linear predictive and noncoherent detection schemes. While good performance for increasing complexity can often be achieved with a finite-memory detection strategy, key issues in the design of detection algorithms are the computational efficiency and the performance for limited complexity.

In this paper, we consider serially concatenated schemes with outer novel and efficient low-density parity-check (LDPC) codes and inner modulations effective against channel impairments. With a pragmatic approach, we show how to design LDPC codes tailored for simple and robust modulation formats, such as differentially encoded (DE) modulations. The LDPC codes are optimized through the use of a recently proposed analysis technique based on extrinsic information transfer (EXIT) charts. In particular, we optimize, through a "clever" random walk in the parametric space, the degree distributions of the outer LDPC codes, obtaining significant insights on the impact of such distributions on the performance of the proposed concatenated schemes. The optimization is carried out for transmission over both the additive white Gaussian noise channel and a noncoherent channel. The performance predicted by the EXIT chart-based optimization is confirmed by results obtained via computer simulations, considering phase-shift keying and quadrature amplitude modulation at the transmitter side, and iterative demodulation/decoding at the receiver side. The significance of the proposed optimized design of LDPC-coded schemes with DE modulations is validated by the fact that standard nonoptimized LDPC codes perform poorly when used together with inner DE modulations.

We propose a general framework for trellis-based detection over channels with infinite memory. A general truncation assumption enables the definition of a trellis diagram, which takes into account a considered portion of the channel memory and possible coding memory at the transmitter side. It is shown that trellis-based maximum a posteriori (MAP) symbol detection algorithms, in the form of forward-backward (FB) algorithms, can be derived on the basis of this memory-truncation assumption. A general approach to the design of truncated-memory (TM) FB algorithms is proposed, and two main classes of algorithms, characterized by coupled and decoupled recursions, respectively, are presented. The complexity of the derived TM-FB algorithms is analyzed in detail. Moreover, it is shown that MAP sequence detection algorithms, based on the Viterbi algorithm, follow easily from one of the proposed classes. Looking backward at this duality between MAP symbol detection algorithms and MAP sequence detection algorithms, it is shown that previous solutions for one case can be systematically extended to the other case. The generality of the proposed framework is shown by considering various examples of stochastic channels. New detection algorithms, as well as generalizations of solutions previously published in the literature, are embedded in the proposed framework. The obtained results do suggest that the performance of the proposed detection algorithms ultimately depends on the truncation depth, almost regardless of the specific detection strategy.

In this paper, we propose a simple semi-analytical approach for the evaluation of the impact of mobility on the bit error rate (BER) performance of multi-hop ad hoc wireless networks. Analytical expressions, relating the BER at the end of a multi-hop route with the mobility characteristics of the nodes and the routing strategy, are derived. Two node mobility models are considered: direction-persistent (DP) and direction-nonpersistent (DNP). In particular, two network switching scenarios are analyzed: (i) opportunistic non-reservation-based switching (ONRBS), where a message flows from source to destination by opportunistically choosing the available shortest consecutive links; and (ii) reservation-based switching (RBS), where, after the creation of a multi-hop route from source to destination, the message is "forced" to flow over the reserved links, regardless of their actual lengths. The network performance is evaluated in ideal (without inter-node interference, INI) and realistic (with INI) cases. The improved robustness against mobility offered by ONRBS, with respect to RBS, is analyzed and quantified.

Determining the minimum number of neighboring nodes required to guarantee full connectivity, i.e., to ensure that a node can reach, through multiple hops, any other node in the network, is an important problem in ad hoc wireless networks. In this paper, we consider reservation-based wireless networks with stationary and uniform (on average) node spatial distribution. Assuming that any communication route is a sequence of minimum length hops, we show that, in an ideal case without inter-node interference (INI) and on the basis of a suitable definition of transmission range, the minimum number of neighbors required for full connectivity is, on average, /spl pi/. Full connectivity is guaranteed if the transmitted power (in the case of fixed node spatial density) or, equivalently, the node spatial density (in the case of fixed transmitted power) are larger than critical minimum values. In a realistic case with INI, we prove that there are situations where full connectivity cannot be guaranteed, regardless of the number of neighbors or the transmitted power.

We consider a novel discrete-time model to analyze the performance of circuit-switched sensor networks. In particular, we assume that a node, after reserving a multi-hop communication route to the desired destination, holds it for a time interval defined as reserved channel utilization interval (RCUI) and utilizes it for an effective channel utilization interval (ECUI). A realistic network communication scenario with inter-node interference (INI) and a reservation-based medium access control (MAC) protocol with finite numbers of (active) routes (FNR) in the network is first considered, and the average interference power is evaluated through a novel combinatorial analysis. Results are presented in terms of effective transport capacity and channel utilization ratio (CUR). In particular, we show that for very low values of the packet generation rate at each node, activation of the maximum possible number of routes guarantees no loss, in terms of effective transport capacity, with respect to an ideal (no INI) scenario. However, this comes at the expense of a very low utilization: in other words, once a multi-hop route has been reserved, its effective utilization time must be a few orders of magnitude lower than the duration of the reservation interval.

The subject of this paper is the asymptotic optimality of finite-memory detection for transmission over a channel characterized by a single multiplicative time-invariant stochastic parameter (e.g., block frequency nonselective fading). It is known that any finite-memory detection algorithm, either trellis-based or graph-based, is characterized by a single basic metric. We present a theorem which proves that this metric tends, asymptotically, to that of a receiver with perfect channel state information

We propose a general framework for detection over channels with infinite memory. A general truncation assumption leads automatically to the definition of a trellis diagram. A general approach to the design of forward-backward (FB) algorithms is proposed and two main classes of FB algorithms (with coupled and decoupled recursions, respectively) are presented. Moreover, it is shown that sequence detection algorithms, in the form of a Viterbi algorithm (VA), follow easily from one of the proposed classes. The generality of the proposed framework is shown by applying it to a few stochastic channels. The performance of the proposed algorithms seems to depend ultimately on the truncation length, almost irrespective of the specific detection strategy.

In this paper, bit error rate (BER) performance and connectivity characteristics of multi-hop ad hoc wireless networks are analyzed under a circuit-switched network communication scheme characterized by the creation of a multi-hop communication route, through intermediate relay nodes, for each source-destination pair. The proposed transmission scheme is packetized yet it does not employ retransmissions: in this sense, it can be considered as a hybrid scheme between circuit switching and packet switching. The ideal limiting performance under the assumption of no inter-node interference (INI) is evaluated. In particular, the concept of minimum spatial energy density is introduced, and quantified with a precise expression in the case of uncoded binary phase shift keying (BPSK) transmission. A realistic scenario with INI is then considered, and two different medium access control (MAC) protocols are proposed: Aloha and "per-route" carrier sense multiple access (PR-CSMA). In both cases, the BER performance is analyzed. Results show that MAC and physical layers are strictly interrelated, and designing one without considering the other may lead to wrong choices in ad hoc wireless network design.

We describe an approach for the identification of good distance spectra for possibly existing binary linear block codes based on linear programming and the MacWilliams-Delsarte (1977, 1972) identities. Specifically, the linear program is defined by an expression characterizing the performance of a potential code in terms of its distance spectrum and constraints imposed by the MacWilliams-Delsarte identities. Using the union bound to characterize performance, our results suggest that the best distance spectrum is not a function of signal-to-noise ratio (SNR) above the cutoff rate SNR and also suggest the existence of several unknown, good codes. Characterizing the performance using the maximum spectral error component of the union bound suggests spectral thinning with decreasing SNR.

In this paper we consider iterative detection over bandpass channels which introduce an unknown phase rotation in the transmitted signal. We first introduce a unified formulation of adaptive forward-backward algorithms for channels with parametric uncertainty, including both recursive and non-recursive estimation strategies, and then apply this framework to a phase noncoherent channel. Two main classes of adaptive forward-backward algorithms are then considered and compared: closed-loop algorithms, which use explicit recursive phase estimation, and open-loop algorithms, which use implicit non-recursive phase estimation. We consider schemes with combined detection and decoding. Pilot symbols are inserted in order to cope with the unknown time-varying channel phase.

A general structure of trellis-based reduced-state soft-input/soft-output (RS-SISO) algorithms for communication systems based on concatenated finite state machines (FSMs) with large memory is presented. Based on forward and backward reduced-state (RS) recursions, a particular structure for the RS-SISO algorithm can be obtained by setting suitable parameters in the general formulation. Two novel RS-SISO algorithms are proposed based on a bi-directional state reduction paradigm. To assess the performance of the proposed RS-SISO algorithms, numerical simulations are conducted for isolated long intersymbol interference with additive white Gaussian noise (ISI/AWGN) channels and a serially concatenated system given by interleaved trellis coded modulation (TCM) over an ISI/AWGN channel. Simulation results show that low-complexity RS-SISO algorithms can approach the performance of a full-state SISO algorithm. Moreover, one of the novel RS-SISO algorithms is found to be robust in all the considered cases.

New bounds are proposed for the Marcum Q-function, which is defined by an integral expression where the 0th-order modified Bessel function appears. The proposed bounds are derived by suitable approximations of the 0th-order modified Bessel function in the integration region of the Marcum Q-function. They prove to be very tight and outperform bounds previously proposed in the literature. In particular, the proposed bounds are noticeably good for large values of the parameters of the Marcum Q-function, where previously introduced bounds fail and where exact computation of the function becomes critical due to numerical problems.

We consider possible solutions for noncoherent decoding of concatenated codes with spectrally efficient modulations. Serially concatenated coding structures and possible schemes derived from turbo trellis coded modulation (T-TCM) are considered. In both cases, at the receiver side we consider joint detection and decoding. Since taking into account an augmented channel memory leads to an intolerable trellis size, we consider a recently proposed state-reduction technique.

We propose a technique to reduce the number of trellis states in BCJR-type algorithms, i.e., algorithms. with a structure similar to that of the well-known algorithm by Bahl, Cocke, Jelinek, and Raviv (1974). This work is inspired by reduced-state sequence detection (RSSD). The key idea is the construction, during one of the recursions in the reduced-state trellis, of a "survivor map" to be used in the other recursion. In a more general setting, two distinct survivor maps could be determined in the two recursions and used jointly to approximate the a posteriori probabilities. Three examples of application to iterative decoding are shown: (1) coherent detection for intersymbol interference (ISI) channels; (2) noncoherent detection based on an algorithm previously proposed by the authors; and (3) detection based on linear prediction for Rayleigh fading channels. As in classical RSSD, the proposed algorithm allows significant state-complexity reduction with limited performance degradation.

We address the use of the extrinsic information generated by each component decoder in an iterative decoding process. The BJCR algorithm proposed by Bahl et al. (1974) and the soft-output Viterbi algorithm (SOVA) are considered as component decoders. In both cases, we consider, in a unified view, various feedback schemes which use the extrinsic information in different fashions. Numerical results for a classical rate-1/2 turbo code and a serially concatenated code transmitted over a memoryless additive white Gaussian noise (AWGN) channel are provided. The performance of the considered schemes leads to interesting remarks about the nature of the extrinsic information.

We propose a technique to reduce the number of trellis states in BCJR-type algorithms, i.e., algorithms with a structure similar to that of the well-known algorithm by Bahl, Cocke, Jelinek and Raviv (1974). This work is inspired by reduced-state sequence detection (RSSD). The key idea is the construction, during one of the recursions, of a "survivor map", on a reduced-state trellis, to be used in the other recursion. Two examples of application of the proposed technique to iterative decoding structures are shown, namely coherent detection over intersymbol interference (ISI) channels and noncoherent detection based on an algorithm previously proposed by the authors.

Previously, noncoherent sequence detection schemes for coded linear and continuous phase modulations have been proposed, which deliver hard decisions by means of a Viterbi algorithm. The current trend in digital transmission systems toward iterative decoding algorithms motivates an extension of these schemes. In this paper, we propose two noncoherent soft-output decoding algorithms. The first solution has a structure similar to that of the well-known algorithm by Bahl et al. (1974), whereas the second is based on noncoherent sequence detection and a reduced-state soft-output Viterbi algorithm. Applications to the combined detection and decoding of differential or convolutional codes are considered. Further applications to noncoherent iterative decoding of turbo codes and serially concatenated interleaved codes are also considered. The proposed noncoherent detection schemes exhibit moderate performance loss with respect to corresponding coherent schemes and are very robust to phase and frequency instabilities.

Colavolpe and Raheli have proposed (see Proc. IEEE Intern. Conf. Univ. Pers. Commun. (ICUPC'98), Florence, Italy, October 1998, and Proc. Global Commun. Conf., (GLOBECOM'98), Sydney, Australia, 1998), noncoherent sequence detection schemes for any coded modulation. These schemes produce hard decisions via a Viterbi algorithm. The current trend in digital transmission systems toward iterative decoding algorithms motivates an extension of these schemes. We propose a noncoherent soft-output algorithm for coded linear modulations whose structure is similar to that of the well-known algorithm by Bahl et al. (1974). The application to the case of combined detection and decoding of differential or convolutional codes is considered and comparisons with noncoherent sequence detection are performed. As an example of application to iterative processing, noncoherent decoding of turbo codes is considered.