Direct optical communication between underwater and aerial nodes such as drones offers a transformative approach for accessing underwater devices and sensors in shallow water environments. Unlike traditional underwater communication systems, which rely on buoys or surface vehicles to relay data between underwater devices and ground stations. Direct waterair optical communication enables greater operational flexibility and cost efficiency by utilizing drones to directly collect data or transmit commands. However, a significant challenge in implementing such systems is their limited reliability and short operational range, typically restricted to a few meters in both water and air. To address this limitation, this paper proposes and implements a LoRa-based modulation scheme for optical water-air communication using software-defined radio (SDR) technology. LoRa, a widely recognized radio frequency (RF) technology, is specifically designed for energy-efficient, low-rate communication, making it a promising candidate for enhancing the reliability of optical links. This paper presents a systematic approach to optimizing system parameters to improve link reliability across various scenarios. The experimental results demonstrate that the implementation of optical LoRa-based modulation offers a significant improvement in performance, achieving a gain of 7 dB to 15 dB compared to traditional Binary Phase Shift Keying (BPSK) modulation. This substantial gain underscores the effectiveness of LoRa-based modulation in enhancing the reliability and robustness of optical water-air communication links.

As the fifth generation (5G) of mobile communication systems are becoming a commercial reality, this paper attempts to investigate the reliability and propagation latency performance of UAV-assisted cellular systems that caters to a scalable, flexible and on-demand solution for differentiated 5G services. The reliability performance of the considered system is evaluated in terms of block error rates (BLER) along with the propagation latency for three classes of services: Ultra Reliable Low Latency Communications (URLLC), enhanced Mobile Broadband (eMBB), and massive Machine Type Communications (mMTC). Considering the impact of fading and shadowing, the closed-form approximate expressions for BLER and propagation latency are evaluated over Rician shadowed fading with various shadowing scenarios. The numerical results reveal important insights related to the achievable reliability performance for three different services under given fading and shadowing severity. Specifically, The tightness of the ap-proximation presented is validated through the Monte-Carlo simulations.

The common and well-accepted architectures for monitoring underwater (UW) environments involve UW drones for collecting information and fixed buoys to relay the information outside the water. Fixed buoys lead to some disadvantages, such as their high cost and limited coverage of UW devices. To address these problems, new architectures are considered in the literature, where the buoys are eliminated and instead aerial drones are used for collecting data from the UW ones. These architectures potentially lead to improved and more flexible UW monitoring and control. The existing literature for these novel architectures includes some channel models for optical wireless communication through the water-air (W-A) interface. The primary drawback of these models lies in the fusion of the received rays. Furthermore, the mathematical analysis is mostly carried out in 2D and then extended to 3D, leading to reduced modeling accuracy. In this paper, an accurate 3D ray-tracing solution is proposed for channel modeling and simulation of communication through W-A, where the rays observed at the receiver through multi-path are properly combined. Numerical analysis reveals insights and suggestions on optical channel gain variation over time, and also some conclusions on the overall communication system design, such as the best depth of UW devices and the best height of aerial drones.

Unmanned Aerial Vehicle (UAV) as a cellular Base Station (BS), referred to as UAV-BS, has become a vital asset for emergency response due to their ability to offer an on-demand, adaptable, and expeditious communication solution. The current literature indicates a notable absence of a unified robotics and communication architecture for prototyping UAV-BS, which utilizes open source software and hardware. Further, current work on positioning algorithms of UAV- BS also fails to adequately address the adaptability of UAV-BS in effectively managing spatio-temporal skewness in traffic demands from UEs. In this backdrop, we present Sky5G, a real-world prototype platform for UAV-BS comprised entirely of open-source soft-ware and hardware components. Sky5G platform relies on a middleware to unify the radio and flight responsibilities of UAV-BS. Through several open-sky experiments of Sky5G, we assess various optimal UAV-BS positioning algorithms in response to ground user distribution and spatio-temporal traffic demands.

Nano-sized unmanned aerial vehicles (UAVs) are ideal candidates for flying Internet-of-Things smart sensors to collect information in narrow spaces. This requires ultra-fast navigation under very tight memory/computation constraints. The PULP-Dronet convolutional neural network (CNN) enables autonomous navigation running aboard a nano-UAV at 19, at the cost of a large memory footprint of 320kB– and with drone control in complex scenarios hindered by the disjoint training of collision avoidance and steering capabilities. In this work, we distill a novel family of CNNs with better capabilities than PULP-Dronet, but memory footprint reduced by up to 168× (down to 2.9kB), achieving an inference rate of up to 139frame/s; we collect a new open-source unified collision/steering 66images dataset for more robust navigation; and we perform a thorough in-field analysis of both PULP-Dronet and our tiny CNNs running on a commercially available nano-UAV. Our tiniest CNN, called Tiny-PULP-Dronet v3, navigates with a 100% success rate a challenging and never-seen-before path, composed of a narrow obstacle-populated corridor and a 180°turn, at a maximum target speed of 0.5m/s. In the same scenario, the SoA PULP-Dronet consistently fails despite having 168× more parameters.

The Federal Aviation Administration (FAA) recently introduced a new standard, namely, remote identification, to improve accountability for unmanned aerial vehicles (UAVs) operations. This rule requires UAV operators to broadcast messages revealing sensitive data, such as identity and location on the wireless channel. However, this leads to security and privacy concerns among UAV operators. Unauthorized parties may easily discover the location and identity of a UAV flying in a specific area and launch attacks on it such as using wireless jamming or tracking its activity. This review investigates and systematizes the main weaknesses affecting the Remote ID capability required of modern UAVs, and the approaches through which attackers can exploit these weaknesses to disrupt safety and accountability. Moreover, this article analyzes current solutions that mitigate privacy issues associated with Remote ID. Finally, we identify multiple challenges that require to be addressed by both industry and academia, and we propose future research directions to improve the security and privacy of UAVs.

There is a definite possibility that, in a recent future, Unmanned Aerial Vehicles (UAVs) will form the backbone of any smart city in terms of automation and networking. One approach to extend the UAVs’ resources spectrum is to provide a mean for them to opportunistically recharge and connect to otherwise unreachable networks: provide Training and Recharge Areas (TRAs). In these dedicated areas, the UAVs could dock to Energy and Data Dispensers (EDD) devices to resupply their batteries and exploit a high-speed connection. To autonomously move through the smart city while accomplishing a set of given tasks but, at the same time, consider visiting the EDDs, is part of a tridimensional trajectory planning problem that needs to be addressed. In this paper, we formally define the combinatorial optimization problem representing the trajectory planning. We consider the case in which more than one UAV can be connected with the same EDD at the same time, by properly addressing the assignment of the bandwidth. Through simulative investigation, realistic values for the solution of the optimization problem are found. The behavior of the proposed model is compared with an ”online” approach that does not require the same resources and knowledge and whose evaluation and comparison with the ”offline” approach are performed through network simulation.

This demonstration introduces SOREO, a system that explores the possibility of extending UAVs autonomy through machine learning. It brings a contribution to the following problem: Having a fleet of drones and a geographic area, how to learn the shortest paths between any point with regards to the base points for optimal and safe package delivery? Starting from a set of possible actions, a virtual design of a geographic location of interest, e.g., a city, and a reward value, SOREO is capable of learning not only how to prevent collisions with obstacles, e.g., walls and buildings, but also to find the shortest path between any two points, i.e., the base and the target. SOREO exploits based on the Q-learning algorithm.

Several use-cases of the Internet of Things (IoT) rely on the development of large-scale Wireless Sensor Networks (WSNs) in harsh environments characterized by limited Internet connectivity and battery-powered operations. In such scenarios, the failure of a single node due to energy depletion or hardware issues may cause network partitions and disrupt partially or completely the system operations until the intervention of a human operator. In this paper, we investigate the usage of Unmanned Aerial Networks (UAVs) to enable sensory data collection and support resilient communications in presence of faulty sensor nodes. More specifically, we study the possibility of replacing the ground devices with UAVs which are able to temporarily restore the multi-hop communication towards the WSN sink. To this aim, we extended the Uhura framework, a platform for robotic networking, with novel features for automatic network partition detection and UAV-sink coordination. Then, we created a small test-bed composed of a Bluetooth Mesh WSN and one drone, and characterized the performance of the UAV-assisted WSN system in terms of packet delivery ratio of the end-to-end data flows.

Crowdsourcing technology enables complex tasks to be solved with the aid of a group of workers in the Internet of Things (IoT). On the one hand, crucial sensing data can be collected and processed to enhance smart IoT applications. On the other hand, crowdsourcing IoT (Crowd-IoT) is still facing threats due to the diverse quality of crowdsourced data, and especially the misbehavior of malicious workers. In this paper, we propose a Stochastic Bayesian Game (SBG) to address the Byzantine Altruistic Rational (BAR) based misbehavior, where workers’ behavioral types can be deduced reasonably and the requestor can perform optimal actions accordingly by taking the long-term gain into consideration. To validate and evaluate the performance of the proposed model, we simulate various scenarios and conduct a comparison with other approaches. The numerical results show the effectiveness and feasibility of our proposed solution.

The papers in this special section focus on embedded microprocessors under wireless transmission in medical imaging lesion detection systems and provides researchers in related fields with opportunities for discussions.

In this paper, a Universal Underwater Software Defined Modem (UniSDM) architecture is proposed that may operate in different modes (acoustic, magnetic induction, optical and RF), in order to utilize the advantages of each mode and accordingly satisfy the requirements of many latest use cases in underwater communication systems. A detailed description of the novel UniSDM architecture is presented first. The novelty of this architecture is its flexibility, i.e., allowing the designers to produce a device that may include any type of modes operating seamlessly and jointly by exchanging data, control and synchronization. Many challenges, including high system costs and coordination between different modes, are addressed in the paper. Moreover, numerical evaluation is conducted to assess the performance of the proposed UniSDM architecture. Finally, the performance evaluation shows that the utilization of the UniSDM allows to decrease the transmission latency and improve the energy efficiency, while maintaining high reliability and robustness in underwater communication systems.

In a swarm of unmanned aerial (UAVs) or ground vehicles (UGVs), nodes can autonomously coordinate their activities and cooperate to accomplish a given task as for instance the data exchange with Internet of Things (IoT) devices. However, due to the unpredictable environmental conditions, wireless communication on the air-to-air, ground-to-air and ground-to-ground links can experience completely different channel conditions. For this reason, several Machine-to-Machine (M2M) communication technologies have been proposed with different Quality of Service (QoS) characteristics in terms of range, bandwidth and energy consumption profile: at the same time, new challenges have arisen from the integration or joint utilization of multiple M2M stacks in heterogeneous robotic environments. In this work, we address such challenges through the design and development of a new framework, called Uhura, that eases the interaction among heterogeneous devices e.g., aerial platforms, ground vehicles, robots, sensors, and more. The Uhura framework provides communication facilities for swarm of UAVs/UGVs by abstracting from the underlying M2M technologies; in addition, it supports automatic selection of the M2M stack on multi-adapter UAVs/UGVs based on QoS requirements of the application. In this paper, we describe the Uhura architecture and its ROS-based implementation. Also, we report some results of two real-world experiments involving (i) a small swarm of UAVs and (ii) a multi-adapter UAV communicating to a ground IoT gateway.

In recent years, many underwater communication applications have been proposed and tested, leading to the Internet of underwater things (IoUT) concept. In the IoUT, sensors may be deployed individually on sea surface and seabed as well as in water at different depths. They also may be integrated into underwater items such as fish, plants, autonomous underwater vehicles (AUVs), remotely operated underwater vehicles (ROVs), and divers. Depending on the application, different connectivity requirements must be satisfied such as data rate, latency, and reliability. However, the underwater communication channels pose a significant challenge to meet these requirements. To accommodate various applications with different service level agreements (SLAs), a multimode (acoustic, optical, and magnetic induction (MI)) communication system is proposed to take advantage of the modes' complementary features. Furthermore, an automatic network slicing (ANS) solution is proposed to provide globally optimized resource management, enhanced quality of service (QoS), simplified network operation, reduced deployment cost, and functional isolation of services. Taking the channel characteristics of the acoustic, optical, and MI communication modes into consideration, an optimization problem is formulated and solved for multimode IoUT (MM-IoUT) to enable admission control, routing, and dynamic resource allocation based on the different SLAs. Numerical analysis is conducted to verify the proposed solution and evaluate its performance.

Preventing the negative effects caused by misbehaving of nodes or malicious intrusions is an essential task in trust management (TM) for the Internet of Things (IoT). Although many TM models have been proposed and developed, the majority of these approaches assign nodes trust scores by means of a single static evaluation mechanism without taking into consideration the heterogeneity of IoT nodes and network segments, and the challenges posed by context awareness and scalability. Thus, an applicable TM model is needed to overcome these limitations. In this paper, a phase-based dynamic TM (PDTM) model is proposed. This model enables nodes’ trust scores to be calculated diversely and dynamically in terms of phases. Finally, numerical results show the effectiveness and the accuracy of the proposed model, and resilience against various types of trust-related attacks (TRA).

This paper formulates the Sport Event Filming with Connectivity Constraints (SEF-C2) problem. The SEF-C2problem is an event coverage problem, which exploits a team of Unmanned Aerial Vehicles (UAVs) over a limited field in order to track the movements of an object (e.g., of the ball) and to deliver a video stream of the events (e.g., ball passes, goals) to the spectators meeting certain timeliness and video quality criteria. Assuming a priori knowledge of the whole sequence of actions, first a novel mathematical model is introduced that determines a sequence of movements for the UAVs, such that the timeliness of the filming is maximized and the total traveled distance is minimized. Then, dynamic, artificial potential function based, distributed UAV movement schemes that have no a priori knowledge of the sequence of game actions are proposed to optimize networking performance. Extensive simulations are used to analyze the performance in terms of video transmission quality and show that the proposed schemes outperform existing schemes.

Internet of Things applications using sensors and actuators raise new privacy related threats, such as drivers and vehicles tracking and profiling. These threats can be addressed by developing adaptive and context-aware privacy protection solutions to face the environmental constraints (memory, energy, communication channel, and so on), which cause a number of limitations for applying cryptographic schemes. This paper proposes a privacy preserving solution in ITS context relying on a game theory model between two actors (data holder and data requester) using an incentive motivation against a privacy concession or leading an active attack. We describe the game elements (actors, roles, states, strategies, and transitions) and find an equilibrium point reaching a compromise between privacy concessions and incentive motivation. Finally, we present numerical results to analyze and evaluate the theoretical formulation of the proposed game theory-based model.

This paper proposes a network architecture and supporting optimization framework that allows Unmanned Aerial Vehicles (UAVs) to perform city-scale video monitoring of a set of Points of Interest (PoI). Our approach is systems-driven, relying on experimental studies to identify the permissible number of hops for multi-UAV video relaying in a noisy 3-D environment. Our architecture itself is innovative in the sense that it defines a mathematical framework for selecting the UAVs for periodic re-charging by landing on public transportation buses, and then `riding' the bus to the successive chosen Pol. Specifically, we show that our UAV scheduler can be modeled as an instance of multicommodity flow problems, and mathematically solved through Mixed Integer Linear Programming (MILP) techniques. Thus, our centralized formulation identifies the UAV, the next bus, and the next PoI, given the information about energy thresholds, the bus routes in the city and their next arrival times, to ensure persistent and reliable video coverage of all PoIs in the city. Finally, our work is validated via emulation of a city environment with live traffic updates from a real bus transportation network.

The merging of networking and control fields has always brought interesting innovations but the tools and structures for proper and easy management of experiments still lag behind. Different solutions have been proposed to handle general control problems and, more in detail, for fine control of UAVs (Unmanned Aerial Vehicles) dynamics. They lack, however, an efficient and detailed network-side simulation, usually available only on dedicated software. On the other hand, current advancements in network simulations suites often do not include an accurate simulation of controlled systems. In the middle 2010s, integrated solutions are still lacking. For these reasons, in this paper we propose a simulation architecture for networked control systems. The architecture is based on well-known solutions in both the fields of networking simulation and UAV control simulation. We integrate them into a compact and efficient solution that shows scalability features and negligible architectural delays, as experimental results demonstrate.

A software suite that is capable to simulate Unmanned Aerial Vehicle (UAV) dynamics and, at the same time, network-side behaviors is still lacking in literature. Despite the recent merge between the fields of networking and control, the existing solutions are either dedicated to only one between the aforementioned topics. In this demonstration paper, we describe a novel simulation architecture to implement distributed networked control systems. Our proposal consists in the integration of already-present and validated solutions into a compact package that features scalability and negligible architectural delays.

Due to their deployment flexibility, Unmanned Aerial Vehicles have been found suitable for many application areas, one of them being air pollution monitoring. In fact, deploying a fleet of Unmanned Aerial Vehicles (UAVs) and using them to take environmental samples is an approach that has the potential to become one of the key enabling technologies to enforce pollution control in industrial or rural areas. In this paper, we propose to use an algorithm called Pollution-driven UAV Control (PdUC) that is based on a chemotaxis metaheuristic and a Particle Swarm Optimization (PSO) scheme that only uses local information. Our approach will be used by a monitoring Unmanned Aerial Vehicle to swiftly cover an area and map the distribution of its aerial pollution. We show that, when using PdUC, an implicit priority is applied in the construction of pollution maps, by focusing on areas where the pollutants' concentration is higher. In this way, accurate maps can be constructed in a faster manner when compared to other strategies. We compare PdUC against various standard mobility models through simulation, showing that our protocol achieves better performances, by finding the most polluted areas with more accuracy, within the time bounds defined by the UAV flight time.

Current wireless access technologies cannot satisfy all the requirements of future vehicular networks, and 5G could provide a solution to the actual limitations enabling super-fast, reliable, and low latency connections. The road to 5G vehicular networks can be considered opened by the Release 14 of Long Term Evolution (LTE), which enables vehicle-to-vehicle (V2V) communications also out of coverage of the eNodeB. This work firstly focuses on radio access of actual and next generation vehicular networks, by highlighting the main limitations and potentialities. Then, it focuses on beaconing for cooperative awareness and compares the performance of LTE when adopted in different fashions, from the actual legacy solution in vehicle-to-infrastructure (V2I) mode to future potential alternatives, such as LTE-V2V, LTE-V2V with short subframe and LTE-V2V with full duplex (FD) radios. Performance results are provided in terms of medium access efficiency and success probability of resource allocation, and show the potential advantages of future solutions, especially those provided by LTE-V2V with FD radios.

We propose a new method, called 3-D image warping-based depth video compression (IW-DVC), for fast and efficient compression of depth images captured by mobile RGB-D sensors. The emergence of low-cost RGB-D sensors has created opportunities to find new solutions for a number of computer vision and networked robotics problems, such as 3-D map building, immersive telepresence, or remote sensing. However, efficient transmission and storage of depth data still presents a challenging task to the research community in these applications. Image/video compression has been comprehensively studied and several methods have already been developed. However, these methods result in unacceptably suboptimal outcomes when applied to the depth images. We have designed the IW-DVC method to exploit the special properties of the depth data to achieve a high compression ratio while preserving the quality of the captured depth images. Our solution combines the egomotion estimation and 3-D image warping techniques and includes a lossless coding scheme that is capable of adapting to depth data with a high dynamic range. IW-DVC operates at a high speed, suitable for real-time applications, and is able to attain an enhanced motion compensation accuracy compared with the conventional approaches. In addition, it removes the existing redundant information between the depth frames to further increase compression efficiency. Our experiments show that IW-DVC attains a very high performance yielding significant compression ratios without sacrificing image quality.

Leveraging the concept of controlled node mobility in this paper we develop an algorithm for tracking and controlling proximity malware propagation in Wireless Sensor Networks (WSN). Our proposal aims at: (i) notifying the nodes of malwarepropagation, (ii) leading a flying robot along a path in the WSNthat guarantees the minimum recovery time to (iii) heal the infected nodes. We formulate the targeted curing problem as a binary integer problem and determine the optimal solution by a central solver. We use the analytical result as a benchmark to evaluate the recovery time of the proposed solution. The achieved results show a satisfactory performance in terms of tracking the presence of an ongoing epidemic and healing the nodes.

This paper focuses on the coverage of specific Zones of Interest that can change dynamically over time by using a swarm of flying robots. The mobility of the flying devices is achieved by the design of two distributed and local algorithms. The first algorithm is based on Particle Swarm Optimization (PSO) and Virtual Forces Algorithm (VFA). We modify the classical PSO approach to propose a totally distributed algorithm, which only requires the flying robots to receive local information from the neighbors to update their velocity and trajectory (PSO-S). This new distributed version of the PSO is combined with a distributed version of the Virtual Forces Algorithm. The second algorithm is a distributed implementation of the VFA (VFA-D). To the best of our knowledge, these two approaches are novel in their distributed character, scalability and implementability on resource-constrained devices. We show that the proposed algorithms are reactive, i.e. able to capture in an effective fashion the events happening within the field even if the position of the events changes over time. To show the effectiveness of the proposed techniques, we perform extensive simulations to compare both the PSO-S and the VFA-D schemes with a centralized version of the VFA. Simulations show the good performance in terms of coverage and traveled distance as well as the high reactivity of both PSO-S and VFA-D when the ZoI changes.

In this paper we focus on the problem of dynamic event coverage. We assume that no knowledge about either event position or duration is given a priori. Nonetheless, the events need to be monitored and covered thanks to mobile wireless sensors. Thus, mobile sensors have to discover the events and move towards a new Zone of Interest (ZoI) when the previous monitored event is over. An efficient, distributed and localized solution of this problem would be immediately exploitable by several applications domains, such as environmental, civil, etc. We propose two novel approaches to deal with dynamic event coverage. The first one is a modified version of the PSO, where particles (mobile sensors, nodes or devices in the following) update their velocity by using only local information coming from their neighbors. In practice, the velocity update is performed by considering neighbors' sensed events. Our distributed version of PSO is integrated with a distributed version of the Virtual Force Algorithm (VFA). Virtual Force approach has the ability to “position” nodes with no overlap, by using attractive and repulsive forces based on the distance between nodes. The other proposed algorithm is a distributed implementation of the VFA by itself. Both techniques are able to reach high levels of coverage and show a satisfying reactivity when the ZoI changes. This output parameter is measured as the capability for the sensors to “follow” a sequence of events happening in different ZoIs. The effectiveness of our techniques is shown through a series of simulations and comparisons with the classical centralized VFA.

This paper addresses the Internet of Things (IoT) paradigm, which is gaining substantial ground in modern wireless telecommunications. The IoT describes a vision where heterogeneous objects like computers, sensors, Radio-Frequency IDentification (RFID) tags or mobile phones are able to communicate and cooperate efficiently to achieve common goals thanks to a common IP addressing scheme. This paper focuses on the reliability of emergency applications under IoT technology. These applications' success is contingent upon the delivery of high-priority events from many scattered objects to one or more objects without packet loss. Thus, the network has to be self-adaptive and resilient to errors by providing efficient mechanisms for information distribution especially in the multi-hop scenario. As future perspective, we propose a lightweight and energy-efficient joint mechanism, called AJIA (Adaptive Joint protocol based on Implicit ACK), for packet loss recovery and route quality evaluation in the IoT. In this protocol, we use the overhearing feature, characterizing the wireless channels, as an implicit ACK mechanism. In addition, the protocol allows for an adaptive selection of the routing path based on the link quality. .

In this paper we want to explore a new approach for security mechanisms design and deployment in the context of Internet of Things (IoT). We claim that the usual approach to security issues, typical of more classical systems and networks, does not grab all the aspects related to this new paradigm of communication, sharing and actuation. In fact, the IoT paradigm involves new features, mechanisms and dangers that cannot be completely taken into consideration through the classical formulation of security problems. The IoT calls for a new paradigm of security, which will have to consider the security problem from a holistic perspective including the new actors and their interactions. In this paper, we propose a systemic approach to security in IoT and explore the role of each actor and its interactions with the other main actors of the proposed scheme.

In this paper, we introduce two families of distributed algorithms to control the movement of groups of flying robots that are monitoring an event by moving over the field where the event takes place, while optimizing some specific objective. In order to show the effectiveness of our algorithms, we formulate the Sport Event Filming (SEF) problem. The objective of the problem is to maximize the satisfaction of event viewers while minimizing the distance traveled by the camera-drones. We propose two families of solutions to solve the dynamic version of the problem, where the flying robots do not have any knowledge of the input sequence and move in reaction to the movements of the protagonists of the event. The first family (Nearest Neighbor)is based on a technique used in robotic systems, whereas the second family (Ball Movement Interception) is designed based on specific characteristics of the SEF problem. We present extensive simulation results for both families in terms of average viewer satisfaction and travelled distance for the flying robots, when several parameters vary.

Power consumption is an essential issue in wireless multimedia sensor networks (WMSNs) due to the elevated processing capabilities requested by the video acquisition hardware installed on the generic sensor node. Hence, node placement scheme in WMSNs greatly impacts the overall network lifetime. In this context, the paper first proposes a suitable hardware architecture to implement a feasible WMS node based on off-the-shelf technology, then it shows how the energy spaced placement of the wireless nodes reduces the energy consumption in comparison with the classical evenly spaced placement, without affecting the video quality of multimedia traffic.

In this paper, controlled mobility is used in a novel fashion in order to achieve the best placement for prolonging the lifetime and for maximizing the energy efficiency of the wireless sensor network. Basically, a new source of energy consumption is introduced when the mobility of nodes is exploited. We design a new mechanism for the nodes, which, by virtualizing the movement phase, makes nodes reach the best placement and, at the same time, it counterbalances the effects of energy wasting caused by the movement. We validated the results by extensive simulation campaigns where different network parameters vary. In all the cases, the performance of the network, in terms of lifetime and energy efficiency, has been found fairly superior to the schemes existing in literature.

Exactly determining the geographical position of a telecommunication device is an open research challenge for the personal and mobile communications community. In this paper we explore the terminal localization problem in a GSM system, and we develop a set of carrier independent solutions. Specifically, we introduce four different techniques, all based on the 6-strongest cells traditionally used in the GSM standard. Through extensive simulations we show that our technique can achieve remarkable results without requiring any additional equipment in the core network or the mobile devices.

Wireless meshing has been envisioned as the economically viable networking paradigm to build up broadband and large-scale wireless commodity networks. Several different mesh network architectures have been conceived by both industry and academia; however many issues on the deployment of efficient and fair transport protocols are still open. In this paper, an adaptive and responsive transport protocol (AR- TP) is proposed for WMNs in order to fairly allocate the network resources among multiple flows, while minimizing the performance overhead. Compared to the classical end-to-end rate control mechanisms, an hop-by-hop congestion control approach is designed to keep track of dynamic multi-hop network characteristics in a responsive manner. In addition, a coarse-grained end-to-end reliability algorithm is integrated with the proposed hop-by-hop congestion control mechanism to provide packet level reliability at the transport layer. Performance evaluation via extensive simulation experiments show that the AR-TP protocol achieves high performance in terms of network throughput and fairness.

3G telecommunications systems use microcellular radio coverage, where a high capacity is requested. The usage of these, so called, hotspot has its advantages, because the smaller the cell the higher the bit rate, but it also carries some disadvantages like the higher interference level and the larger average number of handover. The latter is particularly perceived by vehicular users, because it can cause serious degradations in the provided quality of service. To exploit microcellular coverage advantages and, at the same time, limiting its disadvantages, this paper presents two algorithms which work on users' mobility behaviour profile, in order to estimate their recurrent movements and allocate on the future traversed microcells the requested resources in advance. These algorithms introduce remarkable improvements in the grade of service (C.F. Yu et al., 2002) of vehicular users

In the context of hierarchical mobile IPv6 integrated with a fast handover mechanism, this paper proposes two mobility anchor point (MAP) selection algorithms, both based on the classification of users depending on their mobility. Furthermore, an enhancement of one of the two algorithms is presented; it introduces the concept of bufferization at MAP level. Extensive simulations have been performed to analyse and investigate the algorithm results.

This work presents an enhancement of the hierarchical mobile IPv6 protocol, based on a new concept of "distribution areas" and the assignment of multiple LCoAs. It contributes to low latency and packet loss during intradomain handover events. The size and position of the distribution areas are dynamically adjusted depending on traffic load measurements and terminal/user velocity estimation. Simulation results show that it is possible to trade off packet loss and introduced overhead.