Accurate, low-latency channel modeling is essential for real-time wireless network simulation and digital-twin applications. Traditional modeling methods like ray tracing are however computationally demanding and unsuited to model dynamic conditions. In this paper, we propose AIRMap, a deep-learning framework for ultra-fast radio-map estimation, along with an automated pipeline for creating the largest radio-map dataset to date. AIRMap uses a single-input U-Net autoencoder that processes only a 2D elevation map of terrain and building heights. Trained on 1.2M Boston-area samples and validated across four distinct urban and rural environments with varying terrain and building density, AIRMap predicts path gain with under 4 dB RMSE in 4 ms per inference on an NVIDIA L40S-over 100x faster than GPU-accelerated ray tracing based radio maps. A lightweight calibration using just 20% of field measurements reduces the median error to approximately 5%, significantly outperforming traditional simulators, which exceed 50% error. Integration into the Colosseum emulator and the Sionna SYS platform demonstrate near-zero error in spectral efficiency and block-error rate compared to measurement-based channels. These findings validate AIRMap's potential for scalable, accurate, and real-time radio map estimation in wireless digital twins.

The integration of Wireless Sensor Networks (WSNs) with the Internet of Things (IoT) has significantly broadened the scope of interconnected devices, offering novel solutions and enhancing capabilities in monitoring and control across various sectors. Despite remarkable advancements, reliance on battery-powered wireless devices introduces significant challenges, primarily due to energy constraints that limit the operational lifespan of these networks. This paper addresses these challenges by exploring the efficacy of Wake-up Radio (WuR) technology as a means to enhance energy efficiency. WuR technology allows nodes to remain dormant until communication is necessary, thereby extending the network lifetime without compromising performance. However, limitations such as reduced communication range and data transmission rates pose obstacles to the full realization of WuR potential. Through simulation-based experiments, this study evaluates the performance of a novel protocol, Simple Energy Aware Routing (SEAR), under various WuR configurations, using the GreenCastalia simulator. Our findings demonstrate how optimizing WuR parameters can significantly impact key network performance metrics, suggesting pathways for future WuR technology development to achieve optimal WSN performance within the IoT paradigm. The insights provided aim to inform ongoing research efforts, contributing to the evolution of WSNs as a foundational element of the IoT infrastructure.

Wireless Sensor Networks (WSNs) are pivotal in various applications, including precision agriculture, ecological surveillance, and the Internet of Things (IoT). However, energy limitations of battery-powered nodes are a critical challenge, necessitating optimization of energy efficiency for maximal network lifetime. Existing strategies like duty cycling and Wake-up Radio (WuR) technology have been employed to mitigate energy consumption and latency, but they present challenges in scenarios with sparse deployments and short communication ranges. This paper introduces and evaluates the performance of Unmanned Aerial Vehicle (UAV)-assisted mobile data collection for WuR-enabled WSNs through physical and simulated experiments. We propose two one-hop UAV-based data collection strategies: a naïve strategy, which follows a predetermined fixed path, and an adaptive strategy, which optimizes the collection route based on recorded metadata. Our evaluation includes multiple experiment categories, measuring collection reliability, collection cycle duration, successful data collection time (latency), and node awake time to infer network lifetime. Results indicate that the adaptive strategy outperforms the naïve strategy across all metrics. Furthermore, WuR-based scenarios demonstrate lower latency and considerably lower node awake time compared to duty cycle-based scenarios, leading to several orders of magnitude longer network lifetime. Remarkably, our results suggest that the use of WuR technology alone achieves unprecedented network lifetimes, regardless of whether data collection paths are optimized. This underscores the significance of WuR as the technology of choice for all energy critical WSN applications.

Obtaining access to exclusive spectrum, cell sites, Radio Access Network (RAN) equipment, and edge infrastructure imposes major capital expenses to mobile network operators. A neutral host infrastructure, by which a third-party company provides RAN services to mobile operators through network virtualization and slicing techniques, is seen as a promising solution to decrease these costs. Currently, however, neutral host providers lack automated and virtualized pipelines for onboarding new tenants and to provide elastic and on-demand allocation of resources matching operators' requirements. To address this gap, this paper presents NeutRAN, a zero-touch framework based on the O-RAN architecture to support applications on neutral hosts and automatic operator onboarding. NeutRAN builds upon two key components: (i) an optimization engine to guarantee coverage and to meet quality of service requirements while accounting for the limited amount of shared spectrum and RAN nodes, and (ii) a fully virtualized and automated infrastructure that converts the output of the optimization engine into deployable micro-services to be executed at RAN nodes and cell sites. NeutRAN was prototyped on an OpenShift cluster and on a programmable testbed with 4 base stations and 10 users from 3 different tenants. We evaluate its benefits, comparing it to a traditional license based RAN where each tenant has dedicated physical and spectrum resources. We show that NeutRAN can deploy a fully operational neutral host-based cellular network in around 10 seconds. Experimental results× and the per-user average throughput by 1.73× in networks with shared spectrum blocks of 30 MHz. NeutRAN provides a 1.77× cumulative throughput gain even when it can only operate on a shared spectrum block of 10 MHz (one third of the spectrum used in license-based RANs).

Given their independence from operators and potentially unrestricted range of operations, Autonomous Underwater Vehicles (AUVs) are considered key enablers of a host of applications of the Blue Economy. A critical requirement for AUVs is that of being able to self-localize so that the data they collect are clearly marked with position information. Localization is challenging underwater, as GPS and other technologies that use radio frequencies do not work in water. This has brought to the development of solutions that often involve costly technology and operations that are impractical to use in many situations, such as when swift and affordable localization is required. In this paper, we present a method for localizing AUVs that lends itself to be used in such situations, while providing localization that is as accurate as that from more expensive methods. Our method is based on pre-deployed acoustic beacons (whose coordinates do not need to be known by the AUV) and on mainstream sensors usually available onboard most AUVs. It employs an adaptive Extended Kalman Filter (EKF) that exploits statistical techniques to overcome the inaccuracies of baseline EKF when the noise of the environment or of the instrumentation is time-varying or unknown. We demonstrate the effectiveness of our method for accurate AUV localization through simulations and experiments at sea with an AUV and commercial acoustic transducers. Our results show swift determination of the beacon positions and meter-level localization, suggesting that our method can be effectively used in most underwater applications.

Cellular networks are undergoing a radical transformation toward disaggregated, fully virtualized, and programmable architectures with increasingly heterogeneous devices and applications. In this context, the open architecture standardized by the O-RAN Alliance enables algorithmic and hardware-independent Radio Access Network (RAN) adaptation through closed-loop control. O-RAN introduces Machine Learning (ML)-based network control and automation algorithms as so-calledxAppsrunning on RAN Intelligent Controllers . However, in spite of the new opportunities brought about by the Open RAN, advances in ML-based network automation have been slow, mainly because of the unavailability of large-scale datasets and experimental testing infrastructure. This slows down the development and widespread adoption of Deep Reinforcement Learning (DRL) agents on real networks, delaying progress in intelligent and autonomous RAN control. In this paper, we address these challenges by discussing insights and practical solutions for the design, training, testing, and experimental evaluation of DRL-based closed-loop control in the Open RAN. To this end, we introduce ColO-RAN, the first publicly-available large-scale O-RAN testing framework with software-defined radios-in-the-loop. Building on the scale and computational capabilities of the Colosseum wireless network emulator, ColO-RAN enables ML research at scale using O-RAN components, programmable base stations, and a “wireless data factory.” Specifically, we design and develop three exemplary xApps for DRL-based control of RAN slicing, scheduling and online model training, and evaluate their performance on a cellular network with 7 softwarized base stations and 42 users. Finally, we showcase the portability of ColO-RAN to different platforms by deploying it on Arena, an indoor programmable testbed. The lessons learned from the ColO-RAN implementation and the extensive results from our first-of-its-kind large-scale evaluation highlight the importance of experimental frameworks for the development of end-to-end intelligent RAN control pipelines, from data analysis to the design and testing of DRL agents. They also provide insights on the challenges and benefits of DRL-based adaptive control, and on the trade-offs associated to training on a live RAN. ColO-RAN and the collected large-scale dataset are publicly available to the research community.

Underwater vehicles (UVs) are becoming essential for a vast range of novel commercial, scientific and military applications. These include seabed exploration, monitoring of critical infrastructure and resources, and coastal surveillance. However, usage of UVs is currently beset by limitations preventing them to carry critical equipment, such as agile wireless communication systems, which would facilitate and enable those applications. To overcome these limitations, in this paper we present the design blueprint and evaluation of a wireless UV, obtained by integrating a custom software-defined underwater acoustic networking platform integrated to a UV. We first describe the integration of the platform with a commercially available UV. We then present results from experimental campaigns at sea using our wireless UV to generate datasets for studying the Doppler effect due to mobility. Our results indicate the effectiveness of our design for ease of deployment and dataset generation.

Millimeter wave (mmWave) communications have the potential to dramatically increase the throughput of 5G-and-beyond wireless networks. However, the challenging propagation conditions typical of higher frequencies require expensive base station densification to guarantee reliable Radio Access Networks (RANs). Integrated Access and Backhaul (IAB), a solution where wireless access and backhaul use the same waveform, spectrum, and protocol stack, has been proposed and standardized as a highly effective means of decreasing these costs. While IAB is considered a key enabler for high-frequency RANs, experimental research in this context is hampered by the lack of accessible testing platforms. In this demonstration, we showcase IABEST, a large-scale end-to-end IAB testbed based on open-source software and compatible with off-the-shelf hardware. We show how to deploy IABEST capabilities at scale on Colosseum, a publicly available massive channel emulator. Finally, we show how IABEST can support researchers in data collection and algorithm testing from the highest levels of network abstraction down to scheduling decisions.

This paper investigates various ways of minimizing energy consumption in Wireless Sensor Networks (WSNs). We are interested in those methods and technologies that allow network nodes to drastically decrease energy consumption by turning off their primary communication circuitry (main radio), arguably the main culprit of energy depletion. We consider WSNs whose nodes operate according to pre-set duty cycles and WSNs with nodes featuring very low-power wake-up radio devices. In these scenarios we evaluate the performance of an energy-aware routing protocol, showing that when nodes wake up their neighbors based on their suitability to forward data packets (semantic addressing), energy consumption and network lifetime are remarkably better than when all of a sender neighbors are awoken indistinctly (broadcast addressing) and than when nodes duty cycle. Protocols using semantic addressing achieve network lifetimes that are 10× higher than when broadcast addressing is used and three orders of magnitude better than in duty cycle-based networks. We also observe that semantic addressing keeps data latency at bay, achieving end-to-end latency similar to that in networks with nodes with the radio always on.

Open Radio Access Network (RAN) architectures will enable interoperability, openness, and programmatic data-driven control in next generation cellular networks. However, developing scalable and efficient data-driven algorithms that can generalize across diverse deployments and optimize RAN performance is a complex feat, largely unaddressed as of today. Specifically, the ability to design efficient data-driven algorithms for network control and inference requires at a minimum (i) access to large, rich, and heterogeneous datasets; (ii) testing at scale in controlled but realistic environments, and (iii) software pipelines to automate data collection and experimentation. To facilitate these tasks, in this paper we propose OpenRAN Gym, a practical, open, experimental toolbox that provides end-to-end design, data collection, and testing workflows for intelligent control in next generation Open RAN systems. OpenRAN Gym builds on software frameworks for the collection of large datasets and RAN control, and on a lightweight O-RAN environment for experimental wireless platforms. We first provide an overview of OpenRAN Gym and then describe how it can be used to collect data, to design and train artificial intelligence and machine learning-based O-RAN applications (xApps), and to test xApps on a softwarized RAN. Then, we provide an example of two xApps designed with OpenRAN Gym and used to control a large-scale network with 7 base stations and 42 users deployed on the Colosseum testbed. OpenRAN Gym and its software components are open source and publicly-available to the research community.

This paper investigates the physical performance of mobile data collection systems comprising Unmanned Aerial Vehicles (UAVs) in conjunction with Wake-up Radio (WuR) technology to minimize the energy consumption of data exchange with Wireless Sensor Network (WSN) nodes. We setup data collection experiments using a quad-rotor drone as the UAV and WuR-enabled motes as the communication nodes. Our experiments are calibrated using tests that measure flight time, communication range and the performance of data collection using WuR compared with that of data collection when the mote duty cycles. We confirm that collection using duty cycling consumes far more power and achieves lower reliability than collection using WuR technology. In our ranging experiments we observe that while the Mobile Data Collector (MDC) is flying at an altitude of approximately 5 m, reliability decreases monotonically with horizontal distance, averaging at 75.4% of all data packets being successfully collected, while latency averages at 27 ms. At an altitude of 10 m, reliability drops considerably to an average of 14.33%, while latency increases with horizontal distance, averaging at 71.16 ms.

This work concerns underwater networking with mobile assets, like Autonomous Underwater Vehicles (AUVs), for advanced monitoring and exploration of submerged environments. Particularly, we are interested in enabling an AUV to localize itself while moving underwater by acoustically polling beacon nodes statically deployed at well-known location. Our method only relies on a model of the AUV dynamics, on an on-board depth sensor and on long baseline ranging information. The AUV applies an Extended Kalman Filter to estimate its position, without needing any further local measurements but those of depths. We have evaluated the accuracy of the proposed method via experiments at sea in the shallow waters around the Italian island of Ponza, computing the average distance between the estimated locations of the AUV and its positions as measured by GPS along its trajectory (localization error). In deployments with up to four beacons, our simple method enables AUVs to swiftly self localize with errors never exceeding 3.62m (using only two beacons), 2.65m (three beacons) and 2.45m (four beacons).

With the unprecedented rise in traffic demand and mobile subscribers, real-time fine-grained optimization frame-works are crucial for the future of cellular networks. Indeed, rigid and inflexible infrastructures are incapable of adapting to the massive amounts of data forecast for 5G networks. Network softwarization, i.e., the approach of controlling “everything” via software, endows the network with unprecedented flexibility, al-lowing it to run optimization and machine learning-based frame-works for flexible adaptation to current network conditions and traffic demand. This work presents QCell, a Deep Q-Network-based optimization framework for softwarized cellular networks. QCell dynamically allocates slicing and scheduling resources to the network base stations adapting to varying interference con-ditions and traffic patterns. QCell is prototyped on Colosseum, the world's largest network emulator, and tested in a variety of network conditions and scenarios. Our experimental results show that using QCell significantly improves user's throughput (up to 37.6%) and the size of transmission queues (up to 11.9%), decreasing service latency.

Fifth-generation (5G) systems will extensively employ radio access network (RAN) softwarization. This key innovation enables the instantiation of "virtual cellular networks" running on different slices of the shared physical infrastructure. In this paper, we propose the concept of Private Cellular Connectivity as a Service (PCCaaS), where infrastructure providers deploy covert network slices known only to a subset of users. We then present SteaLTE as the first realization of a PCCaaS-enabling system for cellular networks. At its core, SteaLTE utilizes wireless steganography to disguise data as noise to adversarial receivers. Differently from previous work, however, it takes a full-stack approach to steganography, contributing an LTE-compliant stegano-graphic protocol stack for PCCaaS-based communications, and packet schedulers and operations to embed covert data streams on top of traditional cellular traffic (primary traffic). SteaLTE balances undetectability and performance by mimicking channel impairments so that covert data waveforms are almost indistinguishable from noise. We evaluate the performance of SteaLTE on an indoor LTE-compliant testbed under different traffic profiles, distance and mobility patterns. We further test it on the outdoor PAWR POWDER platform over long-range cellular links. Results show that in most experiments SteaLTE imposes little loss of primary traffic throughput in presence of covert data transmissions (<; 6%), making it suitable for undetectable PCCaaS networking.

Practical experimentation and prototyping are core steps in the development of any wireless technology. Often times, however, this crucial step is confined to small laboratory setups that do not capture the scale of commercial deployments and do not ensure result reproducibility and replicability, or it is skipped altogether for lack of suitable hardware and testing facilities. Recent years have seen the development of publicly-available testing platforms for wireless experimentation at scale. Examples include the testbeds of the PAWR program and Colosseum, the world's largest wireless network emulator. With its 256 software-defined radios, 24 racks of powerful compute servers and first-of-its-kind channel emulator, Colosseum allows users to prototype wireless solutions at scale, and guarantees reproducibility and replicability of results. This tutorial provides an overview of the Colosseum platform. We describe the architecture and components of the testbed as a whole, and we then showcase how to run practical experiments in diverse scenarios with heterogeneous wireless technologies (e.g., Wi-Fi and cellular). We also emphasize how Colosseum experiments can be ported to different testing platforms, facilitating full-cycle experimental wireless research: design, experiments and tests at scale in a fully controlled and observable environment and testing in the field. The tutorial concludes with considerations on the flexible future of Colosseum, focusing on its planned extension to emulate larger scenarios and channels at higher frequency bands (mmWave).

The cellular networking ecosystem is being radically transformed by openness, softwarization, and virtualization principles, which will steer NextG networks toward solutions running on "white box" infrastructures. Telco operators will be able to truly bring intelligence to the network, dynamically deploying and adapting its elements at run time according to current conditions and traffic demands. Deploying intelligent solutions for softwarized NextG networks, however, requires extensive prototyping and testing procedures, currently largely unavailable. To this aim, this paper introduces SCOPE, an open and softwarized prototyping platform for NextG systems. SCOPE is made up of: (i) A ready-to-use, portable open-source container for instantiating softwarized and programmable cellular network elements (e.g., base stations and users); (ii) an emulation module for diverse real-world deployments, channels and traffic conditions for testing new solutions; (iii) a data collection module for artificial intelligence and machine learning-based applications, and (iv) a set of open APIs for users to control network element functionalities in real time. Researchers can use SCOPE to test and validate NextG solutions over a variety of large-scale scenarios before implementing them on commercial infrastructures. We demonstrate the capabilities of SCOPE and its platform independence by prototyping exemplary cellular solutions in the controlled environment of Colosseum, the world's largest wireless network emulator. We then port these solutions to indoor and outdoor testbeds, namely, to Arena and POWDER, a PAWR platform.

The extremely high data rates provided by communications in the millimeter-length (mmWave) frequency bands can help address the unprecedented demands of next-generation wireless communications. However, atmospheric attenuation and high propagation loss severely limit the coverage of mmWave networks. To overcome these challenges, multi-input-multi-output (MIMO) provides beamforming capabilities and high-gain steerable antennas to expand communication coverage at mmWave frequencies. The main contribution of this paper is the performance evaluation of mmWave communications on top of the recently released NR standard for 5G cellular networks. Furthermore, we compare the performance of NR with the 4G long-term evolution (LTE) standard on a highly realistic campus environment. We consider physical layer constraints such as transmit power, ambient noise, receiver noise figure, and practical antenna gain in both cases, and examine bitrate and area coverage as the criteria to benchmark the performance. We also show the impact of MIMO technology to improve the performance of the 5G NR cellular network. Our evaluation demonstrates that 5G NR provides on average 6.7 times bitrate improvement without remarkable coverage degradation.

Current cellular networks rely on closed and inflexible infrastructure tightly controlled by a handful of vendors. Their configuration requires vendor support and lengthy manual operations, which prevent Telco Operators (TOs) from unlocking the full network potential and from performing fine grained performance optimization, especially on a per-user basis. To address these key issues, this paper introduces CellOS, a fully automated optimization and management framework for cellular networks that requires negligible intervention (“zero-touch”). CellOS leverages softwarization and automatic optimization principles to bridge Software-Defined Networking (SDN) and cross-layer optimization. Unlike state-of-the-art SDN-inspired solutions for cellular networking, CellOS: (i) Hides low-level network details through a general virtual network abstraction; (ii) allows TOs to define high-level control objectives to dictate the desired network behavior without requiring knowledge of optimization techniques, and (iii) automatically generates and executes distributed control programs for simultaneous optimization of heterogeneous control objectives on multiple network slices. CellOS has been implemented and evaluated on an indoor testbed with two different LTE-compliant implementations: OpenAirInterface and srsLTE. We further demonstrated CellOS capabilities on the long-range outdoor POWDER-RENEW PAWR 5G platform. Results from scenarios with multiple base stations and users show that CellOS is platform-independent and self-adapts to diverse network deployments. Our investigation shows that CellOS outperforms existing solutions on key metrics, including throughput (up to 86% improvement), energy efficiency (up to 84%) and fairness (up to 29%).

Networking and robotics are increasingly coming together to meet the requirements of applications that only advances in both fields can enable. This paper explores one of these joint applications, namely, using a robotic platform such as an Unmanned Aerial System (UAS) to wirelessly retrieve data produced by the devices of a sensor network. For energy conservation purposes devices operate according to a set duty cycle, or are endowed with wake-up radio transceivers allowing them to transmit and receive data only when needed. We define two simple UAS-aided data collection strategies depending on whether the devices use duty cycling or can be woken up by the visiting UAS. The performance of the two strategies is evaluated by using GreenCastalia, an open source simulator extended to model duty cycles, wake-up radio capabilities and the mobility of the UAS. We compare the two strategies with respect to the amount of data the UAS can collect in its visit, the energy consumption of the devices and the corresponding network lifetime. Our results show the key role of low-cost, low-energy consumption wake-up receivers in providing ways of collecting all data from the sensing devices while consuming a negligible fraction of the energy required to devices operating with a duty cycle. As a result, the lifetime of wake-up radio-based networks is orders of magnitude higher than that afforded to networks with duty cycling: Many decades vs. the very few years of networks with extremely low duty cycles.

Wake-up radio technology is proving to be an effective strategy for bettering energy efficiency of wireless networks. Nodes can transmit wake-up signals via their wake-up radios (WuRs), thereby preventing the unnecessary power drain of their main radio. In this paper we propose a multi-hop wake-up relay for CTP-WuR, the Collection Tree Protocol modified to work with wake-up radios. We implement and evaluate the performance of our multi-hop relay solution in the GreenCastalia simulator, and we compare its performance to that of baseline CTP-WuR. We also implement and evaluate the protocols on a physical testbed. Our results show the advantage of multi-hop relaying over the baseline implementation with respect to key metrics. Particularly, multi-hop relaying decreases average energy consumption to 78% of the baseline and packet delivery time is reduced to an average of 71% of the baseline.

Wake-up radio technology helps to attenuate unnecessary power consumption by allowing a node to keep its main radio off until it is woken up by a signal to an auxiliary low-power radio receiver. In this paper, we evaluate the range performance of an ultra-low power wake-up radio receiver (WuR) integrated into a wireless device suitable for wireless sensor networking deployments. We run several ranging experiments, both indoors and outdoors, where a transmitter sends wake-up sequences to a receiver positioned meters away. We measure the amount of received sequences and whether they incur errors or not. Our experiments show that for distances up to 24m indoors the tested WuR receives more than 96% of the transmitted sequences. The WuR performs slightly better outdoors, with more than 99% of the sequences being received with negligible amounts of errors.

Routing solutions for multi-hop underwater wireless sensor networks suffer significant performance degradation as they fail to adapt to the overwhelming dynamics of underwater environments. To respond to this challenge, we propose a new data forwarding scheme where relay selection swiftly adapts to the varying conditions of the underwater channel. Our protocol, termed CARMA for Channel-aware Reinforcement learning-based Multi-path Adaptive routing, adaptively switches between single-path and multi-path routing guided by a distributed reinforcement learning framework that jointly optimizes route-long energy consumption and packet delivery ratio. We compare the performance of CARMA with that of three other routing solutions, namely, CARP, QELAR and EFlood, through SUNSET-based simulations and experiments at sea. Our results show that CARMA obtains a packet delivery ratio that is up to 40% higher than that of all other protocols. CARMA also delivers packets significantly faster than CARP, QELAR and EFlood, while keeping network energy consumption at bay.

The advent of low-power sensor nodes coupled with intelligent software and hardware technologies has led to the era of green wireless networks. From the hardware perspective, green sensor nodes are endowed with energy scavenging capabilities to overcome energy-related limitations. They are also endowed with low-power triggering techniques, i.e., wake-up radios, to eliminate idle listening-induced communication costs. In this paper, we present a comparative performance evaluation of three different data forwarding strategies for green wireless networks, namely, CTP-WUR, GREENROUTES, and WHARP, which have been shown to outperform previous state-of-art solutions. Through GreenCastalia-based simulations we analyze and provide insights into the impact on performance of diverse forwarding design choices, ranging from traditional tree-based routing (CTP- WUR), to end-to-end energy-driven route selection (GREENROUTES), to the use of sophisticated learning models (WHARP). Results show that tree- based routing obtains lesser packet delivery radio than WHARP, thus indicating that including energy harvesting awareness in route selection results in performance advantages. However, the proactive nature of route computation of CTP-WUR results in faster packet delivery and lower energy consumption, requesting further optimization of the cross-layer forwarding of GREENROUTES and WHARP.

We demonstrate the feasibility of long lasting underwater networking by proposing the smart exploitation of the energy harvesting capabilities of underwater sensor nodes. We define a data routing framework that allows senders to select the best forwarding relay taking into account both residual energy and foreseeable harvestable energy. Our forwarding method, named HyDRO, for Harvesting-aware Data ROuting, is also configured to consider channel conditions and route-wide residual energy, performing network wide optimization via local information sharing. The performance of our protocol is evaluated via simulations in scenarios modeled to include realistic underwater settings as well as energy harvesting based on recorded traces. HyDRO is compared to state-of-the-art forwarding protocols for underwater networks. Our results show that jointly considering residual and predicted energy availability is key to achieve lower energy consumption and latency, while obtaining much higher packet delivery ratio.

Superior performance in wireless sensor networks is obtained by taking key protocol decisions based on the outcome of local learning-based computations, informing nodes on past and expected availability of resources. This paper investigates the impact on protocol performance of local computational requirements of learning techniques. We consider a recent routing solution, named WHARP, which makes decentralized and proactive decisions based on a Markov Decision Process (MDP) that takes into account key parameters of wireless green networks, including energy harvesting capabilities, and wakeup radio technology. We show that in these scenarios solving the MDP incurs energy expenditures by far superior to that required by wireless communication, even at very high data traffic. In order to maintain the performance advantages of the learning-based protocol machinery, we propose a heuristic solution that closely approximates the MDP trading off optimality for considerably lighter computational requirements. We compare the performance of the heuristic-based WHARP (called W-HEU) to that of the MDP-based WHARP that uses the standard Backward Value Iteration (W-BVI) through GreenCastalia-based simulations with real computational energy measurements. Our results show that W-HEU outperforms W-BVI on key metrics such as energy consumption and packet delivery ratio, making up for the lost optimality of BVI through the remarkable energy savings of its lighter computational requirements.

We consider underwater multi-modal wireless sensor networks (UWSNs) suitable for applications on submarine surveillance and monitoring, where nodes offload data to a mobile autonomous underwater vehicle (AUV) via optical technology, and coordinate using acoustic communication. Sensed data are associated with a value, decaying in time. In this scenario, we address the problem of finding the path of the AUV so that the Value of Information (VoI) of the data delivered to a sink on the surface is maximized. We define a Greedy and Adaptive AUV Path-finding (GAAP) heuristic that drives the AUV to collect data from nodes depending on the VoI of their data. For benchmarking the performance of AUV path-finding heuristics, we define an integer linear programming (ILP) formulation that accurately models the considered scenario, deriving a path that drives the AUV to collect and deliver data with the maximum VoI. In our experiments GAAP consistently delivers more than 80 percent of the theoretical maximum VoI determined by the ILP model. We also compare the performance of GAAP with that of other strategies for driving the AUV among sensing nodes, namely, random paths, TSP-based paths and a “lawn mower”-like strategy. Our results show that GAAP always outperforms every other heuristic in terms of delivered VoI, also obtaining higher energy efficiency.

This paper concerns the smart exploitation of multimodal communication capabilities of underwater nodes to enable reliable and swift underwater networking. To contrast adverse and highly varying channel conditions we define a smart framework enabling nodes to acquire knowledge on the quality of the communication to neighboring nodes over time. Following a model-based reinforcement learning approach, our framework allows senders to select the best forwarding relay for its data jointly with the best communication device to reach that relay. We name the resulting forwarding method MARLIN, for MultimodAl Reinforcement Learning-based RoutINg. Applications can choose whether to seek reliable routes to the destination, or whether faster packet delivery is more desirable. We evaluate the performance of MARLIN in varying networking scenarios where nodes communicate through two acoustic modems with widely different characteristics. MARLIN is compared to state-of-the-art forwarding protocols, including a channel-aware solution, a machine learning-based solution and to a flooding protocol extended to use multiple modems. Our results show that a smartly learned selection of relay and modem is key to obtain a packet delivery ratio that is twice as much that of other protocols, while maintaining low latencies and energy consumption.

Green wireless networks are characterized by devices that are pervasively deployed and that harvest energy from the surrounding environment. Devices are also endowed with low-power triggering techniques (e.g., wake-up radios) to obviate costly idle communication times. In this paper, we present a novel data forwarding strategy for green wireless networks that fully exploits the self-powered wake-up radio capabilities of the network nodes. The proposed strategy, named WHARP for Wake-up and HARvesting-based energy-Predictive forwarding, sends data to their destination by making decentralized and proactive decisions based on forecast energy and expected traffic. The performance of WHARP has been compared to that of the Energy Harvesting Wastage-Aware (EHWA) strategy through GreenCastalia-based simulations. Results show that our approach delivers up to 72% more packets, 1.6 times faster, and consuming 58% less energy than EHWA. This is obtained through a learned selection of forwarder relays allowing WHARP nodes to be operational 98% of the time: A 30% improvement over EHWA.

We make the connection between next generation wireless standards (5G) and vehicular communication systems. We advocate the importance of transmissions in the millimeter wave band as the only ones capable to provide the Gbit/s data rates required for raw sensor data exchange among vehicles. In this context, our paper describes methods for deriving channel propagation models via ray-tracing simulations for mmWave transmissions with applications to vehicle-to-everything (V2X) communications. It also addresses aspects related to blockage modeling, the effects of diffuse scattering and multipath fading in urban scenarios.

In this paper we present GREENROUTES, an energy-aware routing protocol for Energy Harvesting-based ("green") Wireless Sensor Networks that leverages self-powered technologies for eliminating the need of energy storage device replacement. GREENROUTES combines energy harvesting and wake-up radios with semantic addressing. Semantic addressing capabilities are effectively used to enhance communication by allowing nodes to selectively wake-up a suitable subset of neighboring nodes. This subset is determined by the distance of nodes from the sink, and, greedily, by the residual energy along routes to the sink. The performance of GREENROUTES has been compared to that of the Energy Harvest Wastage-Aware (EHWA) routing solution in scenarios where all nodes harvest energy from the same source, either sun or wind. Results show that GREENROUTES achieves a packet delivery ratio significantly higher (up to 40%) than EHWA, while delivering packets faster and for less power.

Wireless RF power transmission from dedicated Energy Transmitters (ETs) is emerging as a promising approach to enable battery-less wireless networked sensor systems. However, when data communication and RF energy recharging occur in-band, sharing the RF medium and devoting separate access times for both operations raises architectural and protocol level challenges. This paper proposes a novel method of concurrent transmission of data and energy to solve this problem, allowing ETs to transmit energy and sensors to transmit data in the same band synchronously. Our key idea concerns devising a physical layer modulation scheme that allows the data transmitting node to introduce variations in the envelope of the energy signal at the intended recipient. We implemented a proof-of-concept receiver, modeled and validated through extensive experimentation. We then propose a new physical layer mechanism for guaranteed successful delivery of information in a point-to-point link. Quantitative results demonstrate the feasibility of joint energy-data transfer, along with its associated benefits and tradeoffs.

Simultaneous Wireless Information and Power Transmission (SWIPT) has been proposed as a feasible solution to enable joint power and data transfer for the nodes of a battery-less wireless networked sensor system. Different from existing approaches, where the incident energy is split between decoding and harvesting blocks at the receiver chain, this paper describes the design and implementation of an all-digital receiver circuit. We leverage the internal control signals of the circuit, targeting ultra-low power consumption, low bit-rate applications in SWIPT. A proof-of-concept receiver is modeled, implemented using off-the-shelf hardware, and validated through extensive experiments. Quantitative results demonstrate the benefits of this joint energy-data reception approach through a single receiver chain, offering bit-rates of 400 bps.

Emerging wake-up radio technologies have the potential to bring the performance of sensing systems and of the Internet of Things to the levels of low latency and very low energy consumption required to enable critical new applications. This paper provides a step towards this goal with a twofold contribution. We first describe the design and prototyping of a wake-up receiver (WRx) and its integration to a wireless sensor node. Our WRx features very low power consumption (<; 1.3μW), high sensitivity (up to -55dBm), fast reactivity (wake-up time of 130μs), and selective addressing, a key enabler of new high performance protocols. We then present ALBA-WUR, a cross-layer solution for data gathering in sensing systems that redesigns a previous leading protocol, ALBA-R, extending it to exploit the features of our WRx. We evaluate the performance of ALBA-WUR via simulations, showing that the use of the WRx produces remarkable energy savings (up to five orders of magnitude), and achieves lifetimes that are decades longer than those obtained by ALBA-R in sensing systems with duty cycling, while keeping latencies at bay.

This paper formulates the location-dependent power harvesting rates in generalized 2D and 3D placement of multiple Radio Frequency (RF) Energy Transmitters (ETs) for recharging the nodes of a wireless sensor network (WSN). In particular, we study the distributions of total available and harvested power over the entire WSN. We provide closed matrix forms of harvestable power at any given point in space due to the action of concurrent energy transfer from multiple ETs, explicitly considering constructive and destructive interference of the transmitted energy signals. We also analyze the performance of energy transfer in the WSN through power outage probability, interference, and harvested voltage as a function of the wireless power received from the ETs. Our results reveal that the network wide received power and interference power from concurrent energy transfers exhibit Log-Normal distributions, and the harvested voltage over the network follows a Rayleigh distribution.

A wireless sensor network that employs passive radio wake-up of the sensor nodes can reduce the energy cost for unnecessary idle listening and communication overhead, extending the network lifetime. A passive wake-up radio is powered by the electromagnetic waves transmitted by a wake-up transmitter rather than a battery on the sensor node. However, this method of powering the wake-up radio results in a short wake-up range, which limits the performance of a passive wake-up radio sensor network. In this article, we describe our design of a passive wake-up radio sensor node—REACH 2 -Mote—using a high-efficiency, energy-harvesting module and a very low power wake-up circuit to achieve an extended wake-up range. We implemented REACH 2 -Mote in hardware and performed field tests to characterize its performance. The experimental results show that REACH 2 -Mote can achieve a wake-up range of 44 feet. We also modeled REACH 2 -Mote and evaluated its performance through simulations, comparing its performance to that of another passive wake-up radio approach, an active wake-up radio approach, and a conventional duty cycling approach. The simulation results show that REACH 2 -Mote can significantly extend the network lifetime while achieving high packet delivery rate and low latency.

Wireless transfer of energy through directed radio frequency waves has the potential to realize perennially operating sensor nodes by replenishing the energy contained in the limited on-board battery. However, the high power energy transfer from energy transmitters (ETs) interferes with data communication, limiting the coexistence of these functions. This paper provides the first experimental study to quantify the rate of charging, packet loss due to interference, and suitable ranges for charging and data communication of the ETs. It also explores how the placement and relative distances of multiple ETs affect the charging process, demonstrating constructive and destructive energy aggregation at the sensor nodes. Finally, we investigate the impact of the separation in frequency between data and energy transmissions, as well as among multiple concurrent energy transmissions. Our results aim at providing insights on radio frequency-based energy harvesting wireless sensor networks for enhanced protocol design and network planning.

This paper considers underwater wireless sensor networks (UWSNs) for submarine surveillance and monitoring. Nodes produce data with an associated value, decaying in time. An autonomous underwater vehicle (AUV) is sent to retrieve information from the nodes, through optical communication, and periodically emerges to deliver the collected data to a sink, located on the surface or onshore. Our objective is to determine a collection path for the AUV so that the Value of Information (VoI) of the data delivered to the sink is maximized. To this purpose, we first define an Integer Linear Programming (ILP) model for path planning that considers realistic data communication rates, distances, and surfacing constraints. We then define the first heuristic for path finding that is adaptive to the occurrence of new events, relying only on acoustic communication for exchanging short control messages. Our Greedy and Adaptive AUV Path-finding (GAAP) heuristic drives the AUV to collect packets from nodes to maximize the VoI of the delivered data. We compare the VoI of data obtained by running the optimum solution derived by the ILP model to that obtained from running GAAP over UWSNs with realistic and desirable size. In our experiments GAAP consistently delivers more than 80% of the theoretical maximum VoI determined by the ILP model.

We present a new service discovery algorithm, termed SIDEMAN, which considers human mobility for service dissemination and discovery. SIDEMAN takes advantage of mobile social networking characteristics, such as user membership to a restricted number of communities and interest for similar services among users in the same community. We evaluated the performance of SIDEMAN via simulations in a scenario based on traces collected at the IEEE conference Infocom in 2006. Our algorithm has been compared to the social version of two popular data dissemination techniques, namely, flooding and gossiping. We have measured how proactive an algorithm is in distributing services of interest (Recall), how many services are already with a user when they are needed (Gain), the energy cost for service discovery, and the time needed to reply a service query. We show that SIDEMAN obtains perfect Recall and a Gain that is always comparable to that of the other algorithms. Furthermore, most services are retrieved in reasonable time and at a lower energy cost than that of the flooding and gossiping-based solutions.

Harvesting energy from radio frequency (RF) waves brings us closer to achieving the goal for perpetual operation of a wireless sensor network (WSN) by replenishing the batteries of the sensor nodes. However, due to restrictions on the maximum transmitted power, path loss, and receiver sensitivity, only a small amount of energy can be harvested. While a dedicated RF source alleviates the problem to some extent, novel techniques are required to boost the energy transfer efficiency of the source. In this paper, we provide the first experimental demonstration of multi-path energy routing (MPER) for the case of a sparsely distributed WSNs and show its improved performance over direct energy transfer (DET). In addition, we extend this concept to the case of densely distributed WSNs and experimentally demonstrate and compare the gains obtained by 2- and 3-path energy routing over DET. Our experimental results show that significant energy gains can be achieved in a dense network deployment even when the node to be charged is partially blocked by the neighboring nodes.

This paper presents ALBA-R, a protocol for convergecasting in wireless sensor networks. ALBA-R features the cross-layer integration of geographic routing with contention-based MAC for relay selection and load balancing (ALBA), as well as a mechanism to detect and route around connectivity holes (Rainbow). ALBA and Rainbow (ALBA-R) together solve the problem of routing around a dead end without overhead-intensive techniques such as graph planarization and face routing. The protocol is localized and distributed, and adapts efficiently to varying traffic and node deployments. Through extensive ns2-based simulations, we show that ALBA-R significantly outperforms other convergecasting protocols and solutions for dealing with connectivity holes, especially in critical traffic conditions and low-density networks. The performance of ALBA-R is also evaluated through experiments in an outdoor testbed of TinyOS motes. Our results show that ALBA-R is an energy-efficient protocol that achieves remarkable performance in terms of packet delivery ratio and end-to-end latency in different scenarios, thus being suitable for real network deployments.

We consider an underwater wireless sensor network where baseline communication happens over acoustic, multi-hop routes from the underwater nodes to an on-shore station. The data collected by the nodes greatly exceeds the baseline communication capability. At best, the nodes can transmit digests of their full observations. In order for the sink to receive all sensed data, an autonomous underwater vehicle (AUV) is sent to each node for collecting data over short-distance, high data rate optical connections. The AUV then offloads all collected information to the terrestrial station via wireless communication when it surfaces. The observations made by the nodes vary in size and urgency. The information they provide has an associated value. Given a path of the AUV, we design scheduling strategies for the nodes to decide when and how much information (i.e., which digest) to transmit via acoustic routes so that the value of information reaching the terrestrial station is maximized. These strategies are compared via simulations on realistic scenarios. Our results show that scheduling algorithms that are able to locally estimate the value of information of a data digest provide the delivery of data with a significantly higher value of information. In contrast, uninformed algorithms, i.e., strategies that do not consider the value of information at the node level, provide only a marginal increase over the benchmark case of using only the AUV for data collection.

Use of a passive wake-up radio can drastically increase the network lifetime in a sensor network by reducing or even completely eliminating unnecessary idle listening. A sensor node with a wake-up radio receiver (WuRx) can operate in an extremely low power sleep mode until it receives a trigger signal sent by a wake-up radio transmitter (WuTx). After receiving the trigger signal, the attached WuRx wakes up the sensor node to start the data communication. In this paper, we implement and compare the performance of three passive wake-up radio-based sensor nodes: 1) WISP-Mote, which is a sensor mote that employs an Intel WISP passive RFID tag as the WuRx; 2) EH-WISP-Mote, which combines a novel energy harvester with the WISP-Mote; and 3) REACH-Mote, which uses the energy harvester circuit combined with an ultra-low-power pulse generator to trigger the wake-up of the mote. Experimental results show that the wake-up range and wake-up delay for the EH-WISP-Mote are improved compared with the WISP-Mote, while providing the ability to perform both broadcast-based and ID-based wake-ups. On the other hand, the REACH-Mote, which can only provide broadcast-based wake-up, can achieve a much longer wake-up range than any known passive wake-up radio to date, achieving feasible wake-up at a range of up to 37 ft.

Batteries of field nodes in a wireless sensor network pose an upper limit on the network lifetime. Energy harvesting and harvesting aware medium access control protocols have the potential to provide uninterrupted network operation, as they aim to replenish the lost energy so that energy neutral operation of the energy harvesting nodes can be achieved. To further improve the energy harvesting process, there is a need for novel schemes so that maximum energy is harvested in a minimum possible time. Multi-hop radio frequency (RF) energy transfer is one such solution that addresses these needs. With the optimal placement of energy relay nodes, multi-hop RF energy transfer can save energy of the source as well as time for the harvesting process. In this work we experimentally demonstrate multi-hop RF energy transfer, wherein two-hop energy transfer is shown to achieve significant energy and time savings with respect to the single-hop case. It is also shown that the gain obtained can be translated to energy transfer range extension.

This paper presents SAVE, for Stochastic Analysis and aVailability of Energy, an analytical framework providing closed form expressions for residual energy and lifetime prediction of wireless sensor nodes. SAVE models a wide umbrella of input factors, including channel characteristics, different energy sources and harvesting policies, link layer parameters (e.g., error control and duty cycling) and various data traffic generation models. Our framework uses stochastic semi-Markov models to derive the residual energy distribution for each harvesting node accounting for practically observed temporal variations. We validate the analytical expressions derived by SAVE by means of simulations, and show that SAVE predictions provide a remarkably close match to the simulation results.

We present a new distributed protocol for setting up and maintaining a backbone for multi-hop mobile networks with multiple radio interfaces. Our solution, termed MM-Backs, is effective in producing backbones with limited size, while being robust and reliable in the face of node mobility. We also describe how MM-Backs can be used to transparently support ad hoc routing, without the need to modify a routing protocol to adapt to multiple radios. We compare our protocol with a solution for mobile backbones running in single-radio networks. MM-Backs provides backbones of reasonable size, with superior connectivity, shorter route lengths, higher resilience and lower maintenance-related overhead. When running AODV over MM-Backs backbones we observe a remarkably higher packet delivery ratio and lower overhead than when running AODV over singleradio backbones or by itself.

The way the network topology is modeled in a wireless network, primarily in ad hoc and sensor networks, has a fundamental influence on protocol design and efficiency. The frequently used graph models are simpler, and more amenable to analysis and protocol development. On the other hand, physical models represent the actual radio environment much more faithfully, albeit at the price of being far less supportive to network protocol development. We consider the potential future trend of resolving this conflict, via the integration of the two approaches. We present some results and challenges in exploring the connections between the two apparently very different classes of models.

We investigate the performance of a new distributed protocol for setting up and maintaining a backbone for multi-hop mobile networks with multiple radio interfaces. Our solution, termed MM-Backs, is effective in producing backbones with limited size, while being robust and reliable in the face of node mobility. We compare our protocol with a solution for mobile backbones running in single-radio networks that has been shown to outperform previous backbone formation protocols. MM-Backs provides backbones of reasonable size, with superior connectivity, shorter route lengths, higher resilience and lower maintenance-related overhead.

We present a new distributed protocol for setting up and maintaining a backbone for multi-hop mobile networks. Our solution, termed M-Backs, is effective in producing backbones with limited size, while being robust and reliable in the face of node mobility. We compare our protocol with previous solutions for mobile backbone set up and maintenance. M-Backs outperforms previous protocols, providing backbones of reasonable size, with superior connectivity, shorter route lengths, higher robustness and lower maintenance-related overhead. When running AODV over the backbones M-Backs obtains higher packet delivery ratio and lower latency than previous backbone protocols and than AODV run over the flat network topology.

We address multi-radio networks, i.e., wireless networks where the nodes are equipped with multiple air interfaces. We analyze, both analytically and via simulation, various gains that the multi-radio environment can provide. First we investigate the gain in network connectivity by modeling the topology of a multi-radio network by a multigraph. The gain is captured by introducing the novel graph theoretic concept of the multigraph advantage. It is the surplus of connectivity over the sum of the individual connectivities, as we put together several graphs to form a multigraph sum. We first prove that in the traditional random graph model it results in a strict super-additive behavior. We validate the theoretical results via simulations and show that similar phenomena occur in geometric random graph models. We then investigate, via ns2-based simulations, the nodal energy consumption as well as the end-to-end packet latency needed to route packets in a multi-radio network.

In this paper we present ROME, a geographic routing protocol for wireless sensor networks (WSNs) with mobile nodes. ROME design is suited to deal with communication problems in WSN scenarios with high network dynamics, such as nodal addition, nodal removal and node mobility. In addition, it retains desirable properties of protocols for static WSNs such as using cross-layer techniques for performance optimization, dealing with asynchronous nodal duty cycles, and being able to deal with connectivity dead ends. We define the protocol in details and provide detailed simulation-based performance evaluation of ROME. In scenarios with static and mobile nodes together, our ns2-based experiments show that ROME performs remarkably well with respect to metrics such as packet delivery ratio, energy consumption and end-to-end packet latency.

This paper investigates heuristics to control and coordinate the concurrent movement of multiple sinks for lifetime maximization in a wireless sensor network (WSN). We have developed a centralized heuristic that runs in polynomial time given the solution to the linear program from [1] which provides a provable upper bound to the problem of controlled mobility of multiple sinks. The centralized heuristic solves the sink movement and placement problem obtaining lifetimes that are within 2% of the upper bound. We also define a deployable distributed heuristic for coordinating the motion of multiple sinks through the network. The performance comparison of our heuristics with static sink placement and with random sink mobility shows that our distributed heuristic achieves network lifetimes that are remarkably close to the optimum ones, resulting in significant lifetime improvements over random sink mobility (+77.7%) and statically deployed sinks (+382.4%).

This paper concerns the demonstration of the resilience to localization errors of ALBA-R, a protocol for geographic routing in wireless sensor networks (WSNs). In particular, we show that thanks to a simple yet effective nodal coloring mechanism for handling nodal connectivity holes, ALBA-R achieves the further desirable benefit of being totally resilient to localization errors, which are unavoidable in WSNs. Via ns2-based simulations we show that independently of fundamental network parameters such as network density, and also independently of errors in nodal coordinate estimations as high as the node transmission radius, ALBA-R is successful in delivering all generated packets while incurring reasonable degradation for metrics such as route-length and end-to-end latency and still remaining and energy efficient protocol.

We propose scalable models and centralized heuristics for the concurrent and coordinated movement of multiple sinks in a wireless sensor network (WSN). The proposed centralized heuristic runs in polynomial time given the solution to the linear program and achieves results that are within 2% of the LP-relaxation-based upper bound. It provides a useful benchmark for evaluating centralized and distributed schemes for controlled sink mobility.

Abstract This paper explores the impact of node mobility on distributed and mobility adaptive clustering (DMAC), a typical clustering protocol for mobile ad hoc networks. In particular, in this paper we evaluate the cost of maintaining the DMAC clustering structures when the nodes move according to three different mobility models, namely, the random way point model, the Brownian motion and the Manhattan mobility model. Via ns2‐based simulations, we have observed that the mobility models have different impact on protocol performance. The general trend, however, appears to be the same for networks of increasing size. The second contribution of this paper concerns investigating ways of mitigating the impact of mobility on the clustering structure and hence over the overall network performance. We consider a generalization of DMAC (GDMAC) where rules are established to decrease the number of cluster updates. Via simulation we have observed that GDMAC is effective in reducing the clustering overhead imposed by mobility, and the corresponding maintenance cost. Copyright © 2007 John Wiley & Sons, Ltd.

This demonstration concerns geographic forwarding (GF) as an effective solution for data dissemination (from sensors to a sink) in wireless sensor networks (WSNs). In particular, we focus on demonstrating the different degrees of resilience of a recent solution, ALBA-R, to localization errors, which are highly likely to occur in WSNs. GF routing protocols are based on the nodes knowing their own location information as well as that of the sink, which is the intended destination of a packet.

Among the many ways of improving the performance of a wireless sensor network (WSN) in terms of crucial metrics such as its lifetime and data latency, exploiting the mobility of some of the network components has been recently observed to be among the most promising. In this paper we demonstrate how two very different schemes for WSN mobility leads to different benefits for network performance. More specifically, we consider the data MULEs kind of random, uncontrolled mobility with single-hop data collection and we compare it with the controllable mobility of the data collection point (sink) where sensor-to-sink data routing follows multi-hop paths. Through quite thorough ns2-based simulations we show that data MULEs are to be used in those WSNs deployed for delay tolerant applications. Benefits of this scheme include low energy consumption and easier protocol and nodal design. Data latency, however, can be unbearably high. We therefore show that a good tradeoff between network lifetime gains and data latency increases can be obtained by using those solutions where the mobility of the sink is controlled by the network conditions.

Heterogeneous wireless sensor networks are made up of different kinds of nodes. Some nodes, the sensors, are used as an interface to the physical environment. Other nodes act instead as servers, providing various services to the sensors. In this paper we define an architecture to enable the sensors to efficiently localize the services, and hence the servers. Our is a two-tier server architecture. The first tier is made up of the actual servers. The second tier is formed by nodes that are basically standard nodes (like the sensors). These nodes know the current position of the servers (they are called server locators). Sensors needing service query the server locators to find the corresponding service. The service locator sends a service position to the sensor. Finally, once got a hold of a server location, a sensor uses the service directly. Our server architecture provides load balancing (of queries to the servers) and is tolerant to server faults. Sensor nodes are endowed with caches to maintain the location of popular services. Experiments demonstrate the effectiveness of using caches at the sensor nodes.

This paper concerns the definition of an analytical model and distributed protocols for determining the routes of a mobile data collector (sink) traveling through the nodes of a wireless sensor network (WSN). The routes are determined with the overall aim of maximizing the network lifetime. The contribution of our work is twofold. First, we introduce a novel mixed integer linear programming formulation for determining the sink's route and the sojourn time at the different "sink sites." The model takes into account realistic parameters such as the maximum distance the sink can travel between sites, different sink mobility rates, as well as the costs to support and perform data routing. Solutions to the model provide the route of the sink as a sequence of sites and the sojourn times at those sites that induce the maximum network lifetime. We then propose the Greedy Maximum Residual Energy (GMRE) protocol for sink mobility. GMRE is distributed and localized, thus being suitable for wireless sensor networking. In GMRE the sink greedily keeps moving toward those areas in the network where there is the most residual energy, as if "drawn" to them. This heuristic is then compared with a very simple and energy-unaware protocol where the next site in the sink route is chosen randomly and uniformly each time the sink moves. Simulation results show that GMRE leads to improvements in network lifetime that are four times as much as the lifetime when the sink is kept static, while balancing energy consumption throughout the network.

This paper concerns the comparative performance evaluation of protocols for clustering and backbone formation in ad hoc networks characterized by a large number of resource-constrained nodes. Our aim is twofold: we provide the first simulation-based detailed investigation of techniques for clustering and backbone formation that are among the most representative of this area of ad hoc research. Second, we delve into the nature of the selected protocols to assess the effects of the "degree of localization" on their operations, i.e., how being able to execute the protocol based only on local information affects the overall protocol performance. Extensive ns2-based simulation results show that highly localized protocols are rewarded with good performance with respect to all metrics of interest which include protocol duration, energy consumption, message overhead, route length, and backbone size.

This work concerns the comparative performance evaluation of protocols for clustering and backbone formation in ad hoc networks characterized by a large number of resource-constrained nodes. A typical example of these networks are wireless sensor networks. The DCA protocol represents those protocols whose backbone construction method is based on selecting nodes as clusterheads and then joining them to form a connected backbone. The algorithm proposed by Wu and Li has been chosen to exemplify those algorithms that build a connected backbone and then prune away redundant nodes. Finally, the algorithm by Wan et al. has been considered here for its more theoretical properties of producing a backbone with a constant approximation factor, linear time complexity and optimal message complexity. In order to obtain a backbone reasonably small at reasonable cost we propose an enhancement of the DCA algorithm, termed DCA-S, which enriches the DCA backbone construction with a recently proposed and resource effective sparsification rule. DCA-S leads to a robust backbone close in size to that generated by the Wan et al. protocol without significantly degrading the performance in terms of all the other relevant metrics.

This paper describes the results of an ns2-based comparative performance evaluation among three major solutions presented in the literature for forming multi-hop networks of Bluetooth devices (scatternet formation). The three protocols considered in this paper are BlueTrees, BlueStars, and the "Yao protocol". We observed that device discovery is the most time-consuming operation, independently of the particular protocol to which it is applied. By means of a thorough performance evaluation we have identified protocol parameters and Bluetooth technology features that affect the duration of this device discovery. We have also analyzed the effect of the different protocols operations on key metrics of the generated scatternets. The comparative performance evaluation showed that due to the simplicity of its operations and to its basic working requirements BlueStars is by far the fastest protocol for scatternet formation which also yields to scatternets with a lower number of piconets, average route length and number of roles per node. However, BlueStars produces scatternets with an unbounded, possibly large number of slaves per piconet, which imposes the use of potentially inefficient Bluetooth operations. A good compromise when interested informing scatternets whose piconets have a bounded number of slaves is obtained by combining BlueStars and the Yao protocol. Although latency and route lengths are longer than in BlueStars scatternets, with the combined solution we obtain an overall good protocol performance and scatternets with desired characteristics.

Describes a protocol for the establishment of multihop ad hoc networks based on Bluetooth devices. The protocol proceeds in three phases: device discovery, partitioning of the network into Bluetooth piconets, and interconnection of the piconets into a connected scatternet. The protocol has the following desirable properties: it is executed at each node with no prior knowledge of the network topology, thus being fully distributed. The selection of the Bluetooth masters is driven by the suitability of a node to be the "best fit" for serving as a master. The generated scatternet is a connected mesh with multiple paths between any pair of nodes, thus achieving robustness. Differently from existing solutions, no extra hardware is required to run the protocol at each node and there is no need for a designated node to start the scatternet formation process. Simulation results are provided which evaluate the impact of the Bluetooth device discovery phase on the performance of the protocol.

We describe BlueMesh, a new protocol for the establishment of scatternets, i.e., multihop ad hoc networks of Bluetooth devices. BlueMesh defines rules for device discovery, piconet formation and piconet interconnection in order to achieve the following desirable properties: a) BlueMesh generates connected scatternets without requiring the Bluetooth devices all to be in each other's transmission range; b) the BlueMesh scatternet topology is a mesh with multiple paths between any pair of nodes; c) BlueMesh piconets are made up of no more than 7 slaves. Simulation results in networks with 200 nodes show that BlueMesh is effective in quickly generating a connected scatternet in which each node, on average, does not assume more than 2.3 roles. Moreover, the length of routes between any two nodes in the network, is comparable to that of the shortest paths between the nodes.

This paper describes a new protocol for the establishment of multihop ad hoc networks based on Bluetooth devices. The proposed solution is specification compatible, and achieves the following desirable properties, only a few of which are available in previous solutions. The protocol is executed at each node with no prior knowledge of the network topology, thus being fully distributed. The selection of the Bluetooth masters is driven by the suitability of a node to be the "best fit" for serving as a master. The generated topology (a scatternet, according to the Bluetooth terminology) is a connected mesh with multiple paths between any pair of nodes, thus achieving robustness. In contrast to existing protocols, the proposed solution does not assume any designated device to start the scatternet formation process and it is multihop in the precise sense that there is no requirement for each node to be in the transmission range of all the other nodes (one-hop networks).

Tertio millennio ineunte (at the dawn of the third millennium) it is clear that wireless communications are the natural form of communication among people and even things. Virtually every appliance is equipped with a small, cheap, lightweight wireless interface and the possibility of communicating anytime, from anywhere, to anybody/anything, in the world and beyond, is imminent. In the field of wireless communications and mobile computing, mobile ad hoc networks have received great attention recently. Research into ad hoc networks began in the early 1980s as DARPA packet radio networks. Ad hoc networks operate without any central administration (i.e., base stations and mobile switching centers). Because of its independence from fixed infrastructure, ad hoc networking is considered as the most promising network architecture to enable the ‘many any communications.’ The lack of infrastructure in ad hoc networks, which differentiates ad hoc networks from cellular networks, raises several research challenges. Each network node must act as a router and packet forwarder. Each node communicates via wireless radios that have limited transmission radius. If a node wishes to communicate with another node that is not within its transmission range, it must build a multihop route and rely on intermediate nodes to forward the packet. In addition, each node is mobile and hence the network topology constantly changes in an unpredictable manner. These challenges of ad hoc networks in combination with those of traditional wireless networks (e.g., limited bandwidth, power constraints, security, limited storage) make ad hoc networking research interesting and challenging. Ad hoc networks are deployed in places where it is impossible or difficult to build an infrastructure because of cost, security, and timeliness. Examples of these situations are mostly emergency and military scenarios such as search and rescue, battlefield, and disaster recovery. In recent years, new ad hoc networking technologies, Bluetooth for instance, have emerged and enable personal area networks and home networks to be new application areas of wireless mobile ad hoc networks. With this special issue we bring together seven papers that represent state-of-the-art contributions. The papers published in this issue range from papers that thoroughly survey advanced technologies to papers that report cutting-edge research to papers that describe industrial experiences. The first paper is authored by one of the pioneers in ad hoc networking research, Anthony Ephremides. His paper looks back at the history of ad hoc networks and the growth of this research field. Current research problems are also highlighted in this article. The paper by Bisdikian et al. describes recent mobile communications projects by IBM T.J. Watson Research Center. Their work on Bluetooth technology, BlueDrekar software stack based on Bluetooth specification, and WebSplitter middleware that enables web browsing in multiple devices are introduced. Their interesting vision of tomorrow's web in connected devices is also shared in this paper. The paper by Charles Perkins et al. reports connecting the AODV (Ad hoc On-demand Distance Vector) protocol to the IPv6 Internet. Several technologies are required to achieve this: general Internet gateway connectivity, address auto-configuration, mobile IPv6, changes to router advertisement, changes to treatment of default routes, and multi-gateway operations. These innovations are illustrated in this paper. The following two papers are survey papers. Camp et al. review the mobility models used in ad hoc networking simulations. They categorize the mobility models into entity models where each node's movement is independent of other nodes and group models where a number of nodes in a group share similar mobility patterns. The impact of the selection of mobility models on ad hoc networks performance is studied through extensive simulations. Achieving QoS in ad hoc networks is a difficult task. Perkins and Hughes survey the recent work in this area. They examine the research area in three components: routing, resource reservation, and MAC. The authors also give insights and suggestions for future work for QoS research in ad hoc networking. The paper by Belding-Royer proposes a new hierarchical routing scheme for ad hoc networks called Adaptive Routing along Clustering (ARC). This protocol increases routing flexibility, robustness, and scalability. ARC uses limited broadcasting to minimize the control message overhead. The simulation results show that when combined with AODV, ARC performs favorably over other clustering algorithms. Finally, Kong et al. describe an adaptive security framework for military wireless networks with Unmanned Aerial Vehicles (UAVs). When mobile backbone infrastructure is available, UAVs perform authentication for security services. When the infrastructure is absent, their system localizes the security services at each node for ad hoc communications mode. The paper reports results from their testbed and simulation implementations. We thank the WCMC Editor-in-Chief Mohsen Guizani for his support and the staff at Wiley (Mark Hammond, Laura Kempster, and Claire Bailey). Our deep gratitude also goes to over sixty reviewers for their excellent and detailed reviews. We also thank the authors of all the submitted papers. Enjoy the issue!

We propose a new protocol for efficiently managing large ad hoc networks, i.e., networks in which all nodes can be mobile. We observe that, since nodes in such networks are not necessarily equal in that they may have different resources, not all of them should be involved in basic network operations such as packet forwarding, flooding, etc. In the proposed protocol, a small subset of the network nodes is selected based on their status and they are organized to form a backbone (whence the name "backbone protocol" or simply B-protocol to our proposed solution). The B-protocol operates in two phases: first the "most suitable" nodes are selected to serve as backbone nodes, then the selected nodes are linked to form a backbone which is guaranteed to be connected if the original network is. The effectiveness of the B-protocol in constructing and maintaining in face of node mobility and node/link failure a connected backbone that uses only a small fraction of the nodes and of the links of the original networks is demonstrated via simulation. The obtained results show that both the selected backbone nodes and the links between them in the backbone are considerably smaller than the nodes and the links in the flat network.

Bluetooth is an open specification for short-range wireless communication and networking, mainly intended to be a cable replacement between portable and/or fixed electronic devices. The specification also defines techniques for interconnecting large number of nodes in scatternets, thus enabling the establishment of a mobile ad hoc network (MANET). While several solutions and commercial products have been introduced for one-hop Bluetooth communication, the problem of scatternet formation has not yet been dealt with. This problem concerns the assignment of the roles of master and slave to each node so that the resulting MANET is connected. We introduce two novel protocols for forming connected scatternets. In both cases, the resulting topology is termed a bluetree. In our bluetrees the number of roles each node can assume are limited to two or three (depending on the protocol), thus imposing low slave management overhead. The effectiveness of both protocols in forming MANETs is demonstrated through extensive simulations.

We consider the problem of securing communication in large ad hoc networks, i.e., wireless networks with no fixed, wired infrastructure and with multi-hop routes. Such networks, e.g., networks of sensors, are deployed for applications such as microsensing, monitoring and control, and for extending the peer-to-peer communication capability of smaller group of network users. Because the nodes of these networks, which we term pebbles for their very limited size and large number, are resource constrained, only symmetric key cryptography is feasible. We propose a key management scheme to periodically update the symmetric keys used by all pebbles. By combining mobility-adaptive clustering and an effective probabilistic selection of the key-generating node, the proposed scheme meets the requirements of efficiency, scalability and security needed for the survivability of networks of pebbles (pebblenets)

This paper introduces OLAM, a novel on-demand location aware multicast protocol for ad hoc networks. The protocol assumes that, through the use of positioning system devices, such as Global Positioning System (GPS) devices, each node knows its own position and the current (global) time, and it is able to efficiently distribute these measures, including its current transmission radius, to all other nodes. As the measures are received, each node updates its local snapshot of the complete network topology. When a packet is to be multicast to a group, a heuristic is then used to locally compute the Steiner (i.e., multicast) tree for the addressed multicast group based on the snapshot rather than maintaining the tree in a distributed manner. The resulting Steiner tree is then optimally encoded by using its unique Prufer sequence and included along with the packet, extending the length of the header by no more than the header of packets in source routing (unicast) techniques. All local computations are executed using efficient (i.e., polynomial time) algorithms. The protocol has been simulated in ad hoc networks with 30 and 60 nodes and with different multicast group sizes. We show that OLAM delivers packets to all the nodes in a destination group in more than 85% of the cases. Furthermore, compared to flooding, OLAM achieves improvements of up to 50% on multicast completion delay.

Digital watermarking involves embedding copyright marks (watermarks), often imperceptibly, in multimedia objects to enhance or protect their value. In this paper we describe a novel watermarking algorithm suitable for video coding techniques such as MPEG-4 and H.263/H.324 and we test it in a wireless environment. The proposed algorithm satisfies critical properties not all of which are available in previous solutions. These properties include: resistance (robustness) of the embedded watermark to the error-prone nature of wireless channels as well as to video frame loss or misplacement, negligible probability of reading a non-embedded watermark, non-degradation of the marked video sequence and the possibility to mark video objects (e.g., MPEG-4 objects) in a single frame separately. Experimental results are given that show how these and other properties are achieved when video sequences are corrupted with errors that are typical of a wireless channel.

An algorithm is presented for designing the logical topology of the virtual path (VP) network, an important task in ATM network design. We prove that the algorithm provides a VP network topology that is asymptotically optimal with respect to both connectivity and the diameter of the network. These optimality properties are combined with algorithmic simplicity and polynomial running time, thus overcoming the notorious "optimality vs. scalability" dilemma. This result is made possible by applying the theory of random graphs to this type of networks. This theory has the methodological advantage of increased accuracy with growing network size, thus turning the "curse of dimensionality" into a blessing. Therefore, the paper exemplifies that the theory of random graphs, beyond supporting analysis purposes, may serve as a useful tool in the design of algorithms that overcome the "scalability bottleneck", a problem that prevents current approaches from finding near-optimal solutions as today's networks grow in size and complexity.

This paper proposes a new routing protocol for ad hoc networks built around the source routing technique combined with the location (e.g., GPS coordinates) of nodes obtained by an energy and distance smart dissemination mechanism. The key new observation used is that the location information provides each node with a snapshot of the topology of the complete network from which a source route may be computed locally rather than through route discovery. The resulting protocol has reduced delay, and is more bandwidth and energy efficient, than both traditional (proactive and reactive) ad hoc routing protocols, as well as location based routing protocols.

Broadcast (distributing a message from a source node to all other nodes) is a fundamental problem in distributed computing. Several solutions for solving this problem in mobile wireless networks are available, in which mobility is dealt with either by the use of randomized retransmissions or, in the case of deterministic delivery protocols, by using conflict-free transmission schedules. Randomized solutions can be used only when unbounded delays can be tolerated. Deterministic conflict-free solutions require schedule recomputation when topology changes, thus becoming unstable when the topology rate of change exceeds the schedule recomputation rate. The deterministic broadcast protocols we introduce in this paper overcome the above limitations by using a novel mobility-transparent schedule, thus providing a delivery (time) guarantee without the need to recompute the schedules when topology changes. We show that the proposed protocol is simple and easy to implement, and that it is optimal in networks in which assumptions on the maximum number of the neighbors of a node can be made.

Article Free Access Share on A distance routing effect algorithm for mobility (DREAM) Authors: Stefano Basagni Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas Erik Jonsson School of Engineering and Computer Science, The University of Texas at DallasView Profile , Imrich Chlamtac Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas Erik Jonsson School of Engineering and Computer Science, The University of Texas at DallasView Profile , Violet R. Syrotiuk Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas Erik Jonsson School of Engineering and Computer Science, The University of Texas at DallasView Profile , Barry A. Woodward Erik Jonsson School of Engineering and Computer Science, The University of Texas at Dallas Erik Jonsson School of Engineering and Computer Science, The University of Texas at DallasView Profile Authors Info & Claims MobiCom '98: Proceedings of the 4th annual ACM/IEEE international conference on Mobile computing and networkingOctober 1998 Pages 76–84https://doi.org/10.1145/288235.288254Published:25 October 1998Publication History 704citation5,003DownloadsMetricsTotal Citations704Total Downloads5,003Last 12 Months353Last 6 weeks41 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF