Internet of Underwater Things (IoUT) introduces critical security challenges, especially for protecting distributed infrastructures in resource-constrained environments. Conventional asymmetric and centralized authentication models are unsuitable due to computational and communication overhead, while symmetric approaches lack robustness without trusted storage or hardware. We propose a non-interactive, asynchronous authentication protocol based on NIZKP, combining PUFs-derived secrets with decentralized identifiers on a distributed ledger. This approach enables direct node authentication with cryptographically verifiable identity binding, minimal resource usage, offline verification, and full support for asynchronous operation in constrained environments. The protocol is formally analysed and implemented on COTS hardware without additional secure components. Evaluation shows low energy consumption (827.2 mJ), minimal communication overhead (113 B, 1.513s, 817.9 mJ), and reasonable execution times (worst case ≈ 5.310s), outperforming state-of-the-art solutions in the first four metrics.

The paper describes the Dual Channel Acoustic Communication Protocol (DCAP) and the results of its experimental trials. This open protocol has been developed as an extension of the SWiGacoustic standard proposed by the SWiG Working Group considering requirements and use cases. It enables networking capabilities, simultaneous low and high rate data transfer among acoustic nodes, and frequency division multiple access to the underwater channel. First, the protocol specification and the details of its implementation are described. Then, the methodology for the feasibility testing is presented, followed by the experimental trials at sea in two different environments: confined and open water. The results achieved show the feasibility of using this protocol to allow a network composed of acoustic nodes to simultaneously use low and high rate channels to exchange heterogeneous data through TDMA and FDMA.

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.

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.

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.

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.

Person re-identification (Re-ID) is a challenging task that tries to recognize a person across different cameras, and that can prove useful in video surveillance as well as in forensics and security applications. However, traditional Re-ID systems analyzing image or video sequences suffer from well-known issues such as illumination changes, occlusions, background clutter, and long-term re-identification. To simultaneously address all these difficult problems, we explore a Re-ID solution based on an alternative medium that is inherently not affected by them, i.e., the Wi-Fi technology. The latter, due to the widespread use of wireless communications, has grown rapidly and is already enabling the development of Wi-Fi sensing applications, such as human localization or counting. These sensing procedures generally exploit Wi-Fi signals variations that are a direct consequence, among other things, of human presence, and which can be observed through the channel state information (CSI) of Wi-Fi access points. Following this rationale, in this paper, for the first time in literature, we show how the pervasive Wi-Fi technology can also be directly exploited for person Re-ID. More accurately, Wi-Fi signals amplitude and phase are extracted from CSI measurements and analyzed through a two-branch deep neural network working in a siamese-like fashion. The designed pipeline can extract meaningful features from signals, i.e., radio biometric signatures, that ultimately allow the person Re-ID. The effectiveness of the proposed system is evaluated on a specifically collected dataset, where remarkable performances are obtained; suggesting that Wi-Fi signal variations differ between different people and can consequently be used for their re-identification.

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).

In recent years, underwater communications have seen a growing interest pushed by marine research, oceanography, marine commercial operations, offshore oil industry and defense applications. Generally, underwater communications employ audio signals which can propagate relatively far but are also significantly affected by Doppler distortions. In fact, physical properties of the water and spatial changes due to tides, currents and waves can cause channel variations or unwanted movements of the transmitter or receiver. This study shows how to compensate for the Doppler effect in transmission employing the JANUS standard, a popular modulation scheme for underwater communication. Differently form previous work, we use the pseudo-random symbols of the JANUS preamble to measure and compensate for Doppler distortions without changing the standard. The proposed method has been tested both on the Watermark simulator and real in-field experiments. Results show that the proposed technique allows to correctly receive over 90 % of the packets even with severe Doppler, compensating relative speeds up to 5 m/s.

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.

The Internet of Underwater Things (IoUT) is a novel paradigm for the Blue Economy sector bringing the ability to sense, actuate, exchange information, through low cost technologies, typical of the terrestrial IoT and personal devices, to marine and ocean environments. It requires novel underwater networking concepts that are at the core of the described developed system. While IoUT has already been proposed by the authors for sake of environmental monitoring and marine exploration, in this demo we focus on how IoUT can bring novel opportunities in the field of underwater sport, leisure and tourism. In particular, the demo demonstrates FunDive, a novel wearable system for divers, developed in EC EASME ARCHEOSUb, that supports real-time monitoring of the divers positions and health conditions, at the same time allowing unprecedented enhanced visits of the sites. The system is composed of the following elements: a miniaturized underwater sensor node and an underwater tablet. The miniaturized underwater sensor node hosts an acoustics modem, the underwater networking and information compression logic, as well as a battery pack. The tablet is connected to the node and runs a dedicated APP. The system allows divers to communicate underwater, localize themselves/be localized, navigate to points of interest, and get displayed contents based on their position. A dedicated network protocol provides secure and reliable communication among divers and supports their localization. Our system has been developed by leveraging on the SUNSET Software Defined Communication Stack (S-SDCS) framework and its modular architecture and networking capabilities. In our demonstration we show, through an emulated underwater network environment, the flexibility and effectiveness of our system in allowing divers and surface operators to securely and reliably communicate using underwater tablets and acoustic networking. The navigation and acoustic tracking of divers will be also shown through a novel application interface.

Visible Light Communication (VLC) offers a key alternative to the spectrum-challenged Radio Frequency (RF)-based forms of data transmission by tapping an unutilized and unregulated frequency band. Carefully designed low-cost VLC devices have the potential to enable the Internet of Things (IoT) at scale by reducing the current RF spectrum congestion, which is one of the major obstacles to the pervasiveness of the IoT. Wide adoption of VLC devices is however hindered by their current shortcomings, including low data rate, very short range and inability to communicate in noisy environment. In this paper we describe a new software-defined VLC prototype named VuLCAN for Visible Light Communication And Networking that overcomes these limitations. VuLCAN is based on an ARM Cortex M7 core microcontroller with fast sampling analog-to-digital converter along with power-optimized Digital Signal Processing (DSP) libraries. Using BFSK modulation, the prototype achieves a data rate of 65 Kbps over a communication range of 4.5 m. VuLCAN also provides robust and reliable communications in highly illuminated environments (up to 800 lux) using only a low power Light Emitting Diode (LED), largely exceeding the capabilities of current state-of-the-art prototypes.

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.

This paper concerns the design and implementation of a novel system for diving operations based on an efficient underwater acoustic network. The proposed system is composed of both static underwater nodes and divers that are equipped with acoustic modems and underwater tablets. The SUNSET Software Defined Communication Stack (S-SDCS) has been used to provide multi-hop networking capabilities. Novel image compression and transmission modules have been designed and implemented in S-SDCS. In addition, the localization module has been enhanced to increase the safety of the divers whose positions can be locally and remotely monitored. These modules interact with an innovative mobile application running on the underwater tablet thus enabling divers communication and remote position estimation and monitoring. In addition, divers can acquire and share pictures with other operators in real-time. The user interface of the application has been designed according to diver needs and feedback thus making it perfectly suitable for several diving scenarios. The system has been tested in several at-sea campaigns considering different application scenarios. The achieved results confirm that the proposed system is suitable for several diving experiences, enabling novel application scenarios while increasing divers safety.

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.

Energy-harvesting techniques for low-power embedded devices are opening up new opportunities for the design and optimization of security protocols for Green Wireless Sensor Networks. In this paper, we focus on scenarios where the energy resources of nodes in the network are heterogeneous, and propose a network-level solution that leverages the heterogeneity of harvesting capabilities to reduce the energy consumption of performing costly security operations. Our proposed distributed protocol, called HELIOS (Harvesting- EnabLed computatIon Outsourcing Scheme), allows nodes with scarce energy availability to outsource resource-demanding cryptographic operations to nodes that are harvesting power in excess, resulting in a significant reduction of their energy consumption.

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.

This work is motivated by a general question: can micro-scale energy-harvesting techniques be exploited to support low-cost standard security solutions on resource-constrained devices? We focus on guaranteeing integrity and authentication in Internet of Things (IoT) and Wireless Sensor Network (WSN) applications. In this article, we propose techniques to make ECDSA signatures low cost and implementable on resource-constrained devices. By combining precomputation techniques and energy-harvesting capabilities of modern sensor nodes, we achieve significant improvement over prior works. In addition, we show that the cost of ECDSA signatures can be reduced by up to a factor 10 by using harvesting-aware optimizations.

The combination of low-power design, energy harvesting and ultra-low-power wake-up radios is paving the way for perpetual operation of Wireless Sensor Networks (WSNs). In this work we present the MagoNode++, a novel WSN platform supporting energy harvesting and radio-triggered wake ups for energy- neutral applications. The MagoNode++ features an energy- harvesting subsystem composed by a light or thermoelectric harvester, a battery manager and a power manager module. It further integrates a state-of-the-art RF Wake-Up Receiver (WUR) that enables low-latency asynchronous communication, virtually eliminating idle listening at the main transceiver. Experimental results show that the MagoNode++ consumes only 2.8uA with the WUR in idle listening and the rest of the platform in sleep state, making it suitable for energy-constrained WSN scenarios and for energy-neutral applications.

Wake-up-radio-based sensing systems make use of radio- triggering techniques and ultra-low power wake-up receivers (WuRs) to enable on-demand asynchronous network wake ups. Thanks to this, they have the potential to achieve low latency data collection at minimum energy cost, thus meeting the challenging lifetime and quality-of-service demands of emerging Internet of Things (IoT) and Wireless Sensor Networks (WSNs) applications. However, the fact that nodes can be remotely activated on-demand makes wake-up-radio-based networks vulnerable to energy exhausting attacks. In this paper, with a focus on practical implementation and validation, we present a full-fledged solution to counteract Denial-of-Sleep (DoS) attacks to wake-up-radio-based sensing systems. A core component of our proposed solution is a key exchange protocol based on Elliptic Curve Cryptography (the Fully Hashed MQV protocol), which we use in conjunction with implicit certificates.

The growing number of applications based on Internet of Things (IoT) technologies is pushing towards standardized protocol stacks for machine-to-machine (M2M) communication and the adoption of standard-based security solutions, such as the Datagram Transport Layer Security (DTLS). Despite the huge diffusion of DTLS, there is a lack of optimized implementations tailored to resource constrained devices. High energy consumption and long delays of current implementations limit their effective usage in real-life deployments. The aim of this paper is to explain how to integrate the DTLS protocol inside the Constrained Application Protocol (CoAP), exploiting Elliptic Curve Cryptography (ECC) optimizations and minimizing ROM occupancy. We have implemented our solution on an off-the-shelf mote platform and evaluated its performance. Results show that our ECC optimizations outperform priors scalar multiplication in state of the art for class 1 mote platforms, and improve network lifetime by a factor of up to 6.5 with respect to a standard-based not optimized implementation.

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.

We present a distributed, integrated medium access control, scheduling, routing and congestion/rate control protocol stack for cognitive radio ad hoc networks (CRAHNs) that dynamically exploits the available spectrum resources left unused by primary licensed users, maximizing the throughput of a set of multi-hop flows between peer nodes. Using a network utility maximization (NUM) formulation, we devise a distributed solution consisting of a set of sub-algorithms for the different layers of the protocol stack (MAC, flow scheduling and routing), which result from a natural decomposition of the problem into sub-problems. Specifically, we show that: 1) The NUM optimization problem can be solved via duality theory in a distributed way, and 2) the resulting algorithms can be regarded as the CRAHN protocols. These protocols combine back-pressure scheduling with a CSMA-based random access with exponential backoffs. Our theoretical findings are exploited to provide a practical implementation of our algorithms using a common control channel for node coordination and a wireless spectrum sensor network for spectrum sensing. We evaluate our solutions through ns-2 MIRACLE-based simulations. Our results show that the proposed protocol stack effectively enables multiple flows among cognitive radio nodes to coexist with primary communications. The CRAHN achieves high utilization of the spectrum left unused by the licensed users, while the impact on their communications is limited to an increase of their packet error rate that is below 1 percent.

Energy efficiency has been the driving force behind the design of communication protocols for battery-constrained wireless sensor networks (WSNs). The energy efficiency and the performance of the proposed protocol stacks, however, degrade dramatically in case the low-powered WSNS are subject to interference from high-power wireless systems such as WLANs. In this paper we propose COG-MAC, a novel cognitive medium access control scheme (MAC) for IEEE 802.15.4-compliant WSNS that minimizes the energy cost for multihop communications, by deriving energy-optimal packet lengths and single-hop transmission distances based on the experienced interference from IEEE 802.11 WLANs. We evaluate COG-MAC by deriving a detailed analytic model for its performance and by comparing it with previous access control schemes. Numerical and simulation results show that a significant decrease in packet transmission energy cost, up to 66%, can be achieved in a wide range of scenarios, particularly under severe WLAN interference. COG-MAC is, also, lightweight and shows high robustness against WLAN model estimation errors and is, therefore, an effective, implementable solution to reduce the WSN performance impairment when coexisting with WLANs.

Emerging low-power radio triggering techniques for wireless motes are a promising approach to prolong the lifetime of Wireless Sensor Networks (WSNs). By allowing nodes to activate their main transceiver only when data need to be transmitted or received, wake-up-enabled solutions virtually eliminate the need for idle listening, thus drastically reducing the energy toll of communication. In this paper we describe the design of a novel wake-up receiver architecture based on an innovative pass-band filter bank with high selectivity capability. The proposed concept, demonstrated by a prototype implementation, combines both frequency-domain and time-domain addressing space to allow selective addressing of nodes. To take advantage of the functionalities of the proposed receiver, as well as of energy-harvesting capabilities modern sensor nodes are equipped with, we present a novel wake-up-enabled harvesting-aware communication stack that supports both interest dissemination and converge casting primitives. This stack builds on the ability of the proposed WuR to support dynamic address assignment, which is exploited to optimize system performance. Comparison against traditional WSN protocols shows that the proposed concept allows to optimize performance tradeoffs with respect to existing low-power communication stacks.

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.

Sensor mission assignment involves matching the sensing resources of a wireless sensor network (WSN) to appropriate tasks (missions), which may come to the network dynamically. Although solutions for WSNs with battery-operated nodes have been proposed for this problem, no attention has been given to networks whose nodes have energy-harvesting capabilities and are powered in part by uncontrollable environmental sources, which impose quite a different energy model. In this article we address this problem by providing both an analytical model and a distributed heuristic, called EN-MASSE, specifically tailored for energy-harvesting mission-centric WSNs. To assess the performance of our proposed solution we have interfaced TelosB nodes with solar cells and performed extensive experiments to derive models and traces of solar energy acquisition. We use such real-life traces in our simulations. A comparative performance evaluation between EN-MASSE and other schemes previously proposed in the literature has shown that our solution significantly outperforms existing energy-harvesting-unaware mission assignment schemes. Moreover, using our analytical model as a benchmark, we also show that the profit earned by EN-MASSE is close to the optimum. Finally, we have implemented our proposed solution in TinyOS and experimentally validated its performance, showing the effectiveness of our approach.

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.

Structural health monitoring is a vital tool to help engineers improving the safety of critical structures, avoiding the risks of catastrophic failures. Wireless sensor networks (WSNs) are a very promising technology for structural health monitoring, as they can provide a quality of monitoring similar to conventional (wired) SHM systems with lower cost. In addiction, WSNs are both non-intrusive and non-disruptive and can be employed from the very early stages of construction.The main goal of this work is to investigate the feasibility of a WSN with energy-harvesting capabilities for structural health monitoring, specifically targeting underground tunnels.

The increasing popularity of micro-scale energy-scavenging techniques for wireless sensor networks (WSNs) is opening new opportunities for the development of energy-autonomous systems. To sustain perpetual operations, however, environmentally-powered motes must adapt their workload to the stochastic nature of ambient power sources. Energy prediction algorithms, which forecast the source availability and estimate the expected energy intake in the near future, are precious tools to support the development of proactive power management strategies. In this work, we propose Pro-Energy-VLT, an enhancement of the Pro-Energy prediction algorithm that improves the accuracy of energy predictions, while reducing its memory and energy overhead.

This poster presents the work done to monitor the structural health of a Rome B1 underground construction site through a battery-powered Wireless Sensor Network (WSN). We illustrate the specific requirements and challenges of working with wireless sensors underground, and we describe the solutions adopted for obtaining a working WSN that provides a robust solution of on line monitoring. We conclude by presenting an assessment and testing of these solutions, along with the design insights and the lessons we learned during this on-the-field experience.

The purpose of this demo is to introduce a new low-power wireless device for Wireless Sensor Networks (WSN) operating in the ISM 2.4Ghz band: the MagoNode. Thanks to a highly efficient RF front-end, that extends the radio range and increases link reliability, the MagoNode features out-standing RF performance, still containing energy consumption. The platform has been designed at the Department of Computer, Control, and Management Engineering Antonio Ruberti of the University of Rome La Sapienza in collaboration with the spin-off WSense [1].

This poster describes the main features provided by the Sapienza University Networking framework for underwater Simulation Emulation and real-life Testing (SUNSET) [1]. Particularly, we focus on the innovations and improvements introduced in SUNSET v2 after several years of in field experimentation. All the results collected during the different sea trials (considering sea, fjord, lake and river scenarios) have shown how SUNSET represents a robust, reliable and flexible solutions for in field tests where a network of heterogeneous devices is considered, with different acoustic modems, sensing devices and underwater vehicles.

We demonstrate the use of a novel cross layer protocol for routing in Underwater Acoustic Sensor Networks (UASNs). Our protocol, named CARP [1] for Channel-Aware Routing Protocol, is able to dynamically adapt to channel dynamics and to the changes occurring in the network topology and link quality over time. CARP is implemented and tested using the Sapienza University Networking framework for underwater Simulation Emulation and real-life Testing (SUNSET) [2] in different network scenarios. Our demonstration shows that CARP is able to find the suitable routes and to deliver data to the sink node independently of prevailing packet error rates and of nodes/links addition and removal.

We present a MAC protocol for underwater acoustic sensor networks (UASN) able to overcome the shortcomings induced by the temporary presence of noise sources within, or close-by, the region covered by the UASN. Our solution, named NAMAC for Noise-aware MAC protocol, exploits the ability of nodes equipped with multi-band modems to rapidly switch the frequency band used for communications upon detecting an increase of the in-band noise. Neighboring nodes may collectively decide to migrate to a different band and, as soon as the noise source is no longer impairing communications, to switch back to the default one. NAMAC also ensures connectivity across regions operating on different frequency bands induced by a different impact of noise at different locations. Additionally, if nodes are equipped with acoustic sensors able to monitor low frequencies, i.e., in the range from 0 to 5 kHz, NAMAC can exploit this capability to detect the approaching of a noisy vessel in advance, since noise at low frequencies is audible at larger distances. In this way, nodes can be pre-alerted for a band switch. We perform an extensive comparative performance evaluation of NAMAC based on ns-2 MIRACLE simulations. The noise frequency spectrum used in our simulations reproduces that of existing powerboats. Our performance evaluation shows that NAMAC is able to significantly outperform existing noise unaware MAC protocols that use a single band, increasing the network reliability in the presence of temporary noise sources like passing-by noisy vessels.

ABSTRACT In this paper, we extend previous work on geographic random forwarding (GeRaF) by considering a more efficient paradigm whereby on/off cycles are exploited and every node can participate to packet relaying, as long as it wakes up during any handshake procedure. The protocol is fully distributed, as routing decisions are made hop by hop based on geographic metrics and using a joint channel access/routing procedure, like in the first version of GeRaF. In addition, a previously defined analytical model for GeRaF is used to analyze the protocol, after extending it to cover the new channel access and relay selection procedure. The model is employed to find optimal choices for the parameters of the protocol, before comparing analytical results with ns2 simulations. Results confirm that the geographic paradigm remains a very good solution for energy‐constrained, density‐constrained, and latency‐constrained networks, and that small yet very effective protocol optimizations can achieve remarkable performance improvements. Copyright © 2011 John Wiley & Sons, Ltd.

This paper describes the challenges behind the implementation of a full cross-layer protocol stack for wireless sensor networks. Our solution, IRIS, integrates interest dissemination and convergecasting, adaptive duty cycle and data fusion. The interest dissemination protocol is used for task assignment and to build and maintain the network topology, while convergecasting implements data gathering at the network sink. Convergecasting heavily exploits cross layering in that MAC and routing operations are performed jointly and relay selection is based on flexible cost functions that take into account information from different layers. We have implemented and tested IRIS on two different testbeds, in a controlled lab scenario and in a building with Wi-Fi interference. The flexibility introduced by the IRIS cross layer approach results in higher robustness and reliability than that of well-known approaches such as BoX-MAC combined with the CTP protocol. IRIS delivers more than 95% of packets while other approaches have packet delivery ratios as low as 87% and are not as scalable with traffic. IRIS is also able to support timely and robust delivery of data with duty cycles as low as 5%.

We consider the problem of efficient and fast identification of mobile tags in RFID networks. So far only a few works have addressed identification of mobile tags, and in very specific scenarios (i.e., tags placed on a moving conveyor). In this paper we address more general scenarios, involving tags that are free to move and may stay in the reader range for very short time (e.g., a few seconds), making their identification a real challenge for the reader. We propose a protocol, called PrIME (for Priority-based tag Identification in Mobile Environments), that is based on a probabilistic model and performs continuous reading cycles during which tags may enter and leave the system at any time. Through extensive ns2-based simulations we show that PrIME is very efficient, as it is able to identify 98-99% of mobile tags and to reduce the identification delay drastically with respect to other protocols.

We consider a cognitive radio scenario where the communication between two secondary users (SUs) exploits opportunistic spectrum access (OSA) over a wireless channel licensed to primary users (PUs). Assuming a slotted MAC over a single frequency channel and imperfect spectrum sensing, we address the problem of determining the packet size and Forward Error Correction (FEC) coding rate that maximize the SU communication goodput, i.e., the amount of payload bits correctly received in the unit time. Assuming a Markovian model for the PU activity, a saturation regime for the SU, and periodic channel sensing, we find out the mean time between two consecutive packet transmissions and derive approximate analytical expressions, in closed form, which provide upper and lower bounds on the SUs goodput. Such expressions show the dependence of the SU goodput on packet size, FEC coding rate, signal to noise plus interference ratio, amount of resources allocated to sensing, packet overhead, and primary traffic statistics. We provide simulation results showing that the derived analytical expressions are very close to the actual system performance. We also evaluate the sensitivity of the optimal packet size and FEC coding rate pair to the operational conditions represented by the received power and the PU traffic load, showing that the optimal pair is very sensitive to the former, and only moderately affected by the latter.

Energy harvesting is one of the most promising technologies towards the goal of perpetual operation of wireless sensor networks (WSNs). Environmentally-powered systems, however, have to deal with the variable behavior of ambient energy sources, which results in different amounts and rates of energy available over time. To alleviate the problem of the harvested power being neither constant nor continuous, energy prediction methods can be employed. Such models forecast the source availability and estimate the expected energy intake, allowing the system to take critical decisions about the utilization of the available energy. In this work, we present a novel energy prediction model, named Pro-Energy (PROfile energy prediction model), for multi-source energy harvesting WSNs, which is able to leverage past energy observations to provide accurate estimations of future energy availability. To assess the performance of our proposed solution, we use real-life solar and wind traces that we collected by interfacing TelosB nodes with solar cells and wind micro-turbines, as well as public available traces of solar and wind obtained from weather monitoring stations in the US. A comparative performance evaluation between Pro-Energy and energy predictors previously proposed in the literature, such as EWMA and WCMA, has shown that our solution significantly outperforms existing algorithms for both short and medium term prediction horizons, improving the prediction accuracy up to 60%.

Underwater sensor networks have become an important area of research with many potential practical applications. Given impairments of optical and radio propagation, acoustic communication is used for underwater networking, which translates into variable and long propagation delays, low data rates, long interference ranges and significant fluctuations in terms of link quality over time. A complete characterization of the unique features of the acoustic channel introduces significant complexity both in analytical models and in simulators but is needed for correct characterization of underwater protocols performance. Our objective has been that of designing scalable analytical techniques which are able to derive optimum traffic scheduling and routing for underwater sensor networks while accurately capturing underwater channels features. Specifically the paper presents an analytical model for joint MAC and routing optimization which produces the optimum solution for small to medium scale underwater networks. Scalable, centralized heuristics are then designed, which combine approximate analytical models and scheduling heuristics, and are able to generate solutions close to the optimum. The overall result is a powerful tool to derive benchmark results (upper bounds) for underwater protocol performance and to understand the tradeoffs and performance limits of such systems.

Efficient communication in the crowded ISM band requires the communication networks to be aware of the networking environment and to control their communication protocols accordingly. In this paper we address the issue of efficient WSN communication under WLAN interference. We propose analytic models to describe the WLAN idle time distributions as observed by the WSN nodes, together with efficient methods for parameter estimation. We evaluate how the spectrum sensing capability of the sensors affects the performance of the idle period distribution estimation and conclude that the proposed solutions are accurate enough to support cognitive WSNs.

In order to prolong the lifetime of a wireless sensor network (WSN) devoted to monitoring an area of interest, a useful means is to exploit network redundancy, activating only the sensors that are strictly necessary for coverage and making them work with the minimum necessary sensing radius. In this article, we introduce the first algorithm that reduces sensor coverage redundancy through joint Sensor Activation and sensing Radius Adaptation (SARA) in general application scenarios comprising two classes of devices: sensors with variable sensing radius and sensors with fixed sensing radius. This device heterogeneity is explicitly addressed by modeling the coverage problem through Voronoi-Laguerre diagrams that, differently from Voronoi diagrams, allow for correctly identifying each sensor coverage region depending on the sensor current radius and the radii of its neighboring nodes. SARA executes quickly with guaranteed termination and, given the currently available nodes, it always guarantees maximum coverage. By means of extensive simulations, we show that SARA obtains remarkable improvements with respect to previous solutions, ensuring, in networks with heterogeneous nodes, longer network lifetime and wider coverage.

In this paper we investigate interference cancellation to faster identify tags in RFID networks. We explore how interference cancellation can be applied to ALOHA and tree-based identification schemes, its limitations, the extent of achievable improvements, and the overhead incurred to obtain effective gains. Analytical and simulation results show that for an ALOHA-based scheme interference cancellation allows us to identify nearly 23% of tags without directly interrogating them. This speeds up tag identification (over 20% faster) while producing little overhead. For a tree-based scheme nearly 50% of the tags are identified by exploiting interference cancellation, resulting in an improvement of the identification rate of over 20%. Finally, we propose an enhancement of the tree-based scheme with interference cancellation that achieves a further identification speed up of 50%.

Sensor mission assignment concerns matching the sensing resources of a wireless sensor network (WSN) to appropriate tasks (missions), which may come to the network dynamically. Although solutions for WSNs with battery-operated nodes have been proposed for this problem, no attention has been given to networks whose nodes have energy harvesting capabilities, which impose quite a different energy model. In this paper we address this problem by providing both an analytical model and a distributed heuristic, called EN-MASSE, for energy harvesting WSNs. The objective of both model and EN-MASSE is to maximize the profit of the network, fully exploiting the harvesting technologies, while ensuring the execution of the most critical missions within a given target WSN lifetime. The performance of EN-MASSE is evaluated by simulations based on real solar energy traces. Our experiments show that EN-MASSE behaves very closely to the optimum provided by our model and significantly outperforms previously proposed solutions.

Supporting secondary users through a collateral network dedicated to spectrum sensing has been recently proposed as a mean to overcome spectrum sensing limitations of secondary devices. Building on this idea, we propose a protocol for a Wireless Sensor Network (WSN) to support secondary devices of an Opportunistic Spectrum Access (OSA) network. We examine the fundamental tradeoff between the statistics of the primary network traffic for which the WSN is able to support the OSA network and the interference range or, equivalently, the transmit power of secondary transmitters. We optimize the parameters of the proposed protocol to achieve the minimum delay for delivering channel availability information to secondary users, thus maximizing the portion of time they can effectively use idle primary channels to send their data. We further provide simulation results to demonstrate the feasibility and effectiveness of our design, and point at possible research directions to improve the proposed system capabilities.

One of the major challenges in the use of Radio Frequency-based Identification (RFID) on a large scale is the ability to read a large number of tags quickly. Central to solving this problem is resolving collisions that occur when multiple tags reply to the query of a reader. To this purpose, several MAC protocols for passive RFID systems have been proposed. These typically build on traditional MAC schemes, such as aloha and tree-based protocols. In this paper, we propose a new performance metric by which to judge these anticollision protocols: time system efficiency. This metric provides a direct measure of the time taken to read a group of tags. We then evaluate a set of well-known RFID MAC protocols in light of this metric. Based on the insights gained, we propose a new anticollision protocol, and show that it significantly outperforms previously proposed mechanisms.

GENESI develops structural health monitoring systems for critical infrastructures such as tunnels, bridges, dams, private and public buildings, providing cutting edge green wireless sensor networks technology. The main goal of the project is that of overcoming once and for the barriers that make current wireless sensor network-based monitoring systems unfit for many applications. GENESI will provide solutions for sensor network technology enabling virtually infinite network lifetime. The resulting Green sEnsor NEtworks for Structural monitoring will be truly pervasive, robust and will be able to automatically adapt to application requirements and end-users demands. This poster paper provides an agile synopsis of the structure, aims and objectives of GENESI, providing also its vision for structural health monitoring and expected outcomes.

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%).

Recently RFID technology has made its way into end-user applications, enabling automatic item identification without requiring line of sight. In particular passive tags provide a promising, low cost and energy-efficient solution for inventory applications. However, their large-scale adoption strictly depends on the efficiency of the identification process. A major challenge is how to arbitrate channel access so that all tags are able to answer the reader inquiries and identify themselves over time. This paper stems from the observation that a variety of anti-collision protocols for RFIDs have been proposed in the literature. However, a thorough simulation comparison among them and a clear identification of the mechanisms resulting in better end- to-end performance is lacking. The objective of our work has been to fill this gap. This paper presents the results of a detailed ns2-based comparative evaluation of representatives of all the classes of anti-collision protocols so far proposed. Simulation results show that end-to-end performance of the different classes of protocols in terms of metrics such as the time needed for tags identification differ significantly over what previously found by experiments which only focused on the number of reading cycles for tag identification. Our thorough performance evaluation has highlighted that different solutions are to be used in different application scenarios and that decreasing the collisions (rather than idle times) is the way to go to further improve anti-collision protocols performance.

Research on wireless sensor networks (WSNs) has recently moved from the design and evaluation of protocol stacks to real-life implementation and test of the proposed solutions. It would be desirable to be able to run the implementation of a given protocol stack (or of multiple stacks) over different real-life test-beds obtaining comparable results. This requires standardized interfaces to remotely access and control a federation of test-beds, tools to gather data on the protocol performance without affecting the network operations, as well as ways to standardize how data needed to compute the metrics of interest are gathered and analyzed. This paper describes the design and use of JAMES, a new Test-bed Management System for WSNs that addresses all these challenges. We also discuss the use of JAMES features to ease the experimental evaluation of protocol stacks we have developed. Our experiments show that only a few tens of lines need to be written to be able to execute tests on a given protocol stack and that JAMES operations are transparent to the stack behavior.

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.

The emergent commercial use of techniques for Radio Frequency-based IDentification of different items (RFID) requires the investigation and testing of collision resolution mechanisms for the efficient and correct communication between the system reader and the tags labeling the items that need to be identified. Several MAC protocols have been proposed to resolve collisions in RFID networks. A recent solution, named Tree Slotted Aloha (TSA), has been shown to outperform previous ones with respect to the time it takes for identifying all tags, and the total number of bits transmitted to complete the identification process. However, almost half of the time needed by TSA for identifying tags is spent in collisions. This depends on TSA operation and in particular on the way TSA estimates the number of colliding tags. We have observed that in the case of realistically large networks, TSA highly underestimates this number, with non-negligible impact on the protocol performance. In this paper, we propose a Dynamic Tree Slotted Aloha (Dy TSA) protocol that exploits the knowledge acquired during ongoing readings to refine the estimation of the number of colliding tags. In so doing, Dy TSA adapts the length of the following reading cycles to the actual number of tags still requiring identification. Through ns2-based simulations we show that the proposed method is effective for tag identification and results in significantly improved performance over TSA. Specifically, the length of the identification process is up to 20% lower than that of TSA. Furthermore, the amount of transmitted bits needed for identifying all tags decreases up to 30%.

Geographic forwarding in wireless sensor networks (WSN) has long suffered from the problem of bypassing "dead ends," i.e., those areas in the network where no node can be found in the direction of the data collection point (the sink). Solutions have been proposed to this problem, that rely on geometric techniques leading to the planarization of the network topology graph. In this paper, a novel method alternative to planarization is proposed, termed ALBA-R, that successfully routes packets to the sink transparently to dead ends. ALBA-R combines nodal duty cycles (awake/asleep schedules), channel access and geographic routing in a cross-layer fashion. Dead ends are dealt with by enhancing geographic routing with a mechanism that is distributed, localized and capable of routing packets around connectivity holes. An extensive set of simulations is provided, that demonstrates that ALBA-R is scalable, generates negligible overhead, and outperforms similar solutions with respect to all the metrics of interest investigated, especially in sparse topologies, notoriously the toughest benchmark for geographic routing protocols.

The content delivery networks (CDN) paradigm is based on the idea to transparently move third-party content closer to the users. More specifically, content is replicated on CDN servers which are located close to the final users, and user requests are redirected to the "best" replica (e.g., the closest) in a transparent way, so that users perceive a better content access service. In this paper we address user requests redirection and replica placement in CDNs. Differently from previous solutions our scheme considers the two problems jointly and relies on distributed and localized schemes that can be implemented with little complexity and overhead, thus providing a new overall solution that effectively trades-off among the number of replicas, their utilization (i.e., how many users requests they serve), the distance from the best replica and the number of replica adds and removals. An OPNET based thorough performance evaluation has allowed us to assess the effectiveness of the proposed solution. By properly tuning the distributed heuristics parameters the CDN provider can have a strict control on the CDN network operations so that the desired trade-off between all the relevant performance metrics is achieved.

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.

The paper presents IRIS, an Integrated Routing and Interest dissemination System for wireless sensor networks. The proposed protocols are designed to work under very low duty cycle operations and are jointly optimized for improved efficiency. Routing towards the sink is achieved by exploiting hop count information which is proactively distributed during the interest dissemination phase. Node densities are locally and dynamically estimated at each node and exploited at the MAC layer by means of a cost based probabilistic scheme. A cross-layer routing/MAC scheme is defined where relays to the sink are selected based on nodes' resources (including energy and queue occupancy). The proposed solution is a step towards the definition of complete, self-adapting and autonomous sensor network systems.

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.

The content delivery networks (CDN) paradigm is based on the idea to move third-party content closer to the users transparently. More specifically, content is replicated on servers closer to the users, and users requests are redirected to the best replica in a transparent way, so that the user perceives better content access service. In this paper we address the problem of dynamic replica placement and user requests redirection jointly. Our approach accounts for users demand variability and server constraints, and minimizes the costs paid by a CDN provider without degrading the quality of the user perceived access service. A non-linear integer programming formulation is given for the replica placement and user request redirection problems. The actual solution is obtained by mapping the non-linear integer problem into a series of mixed integer linear problems obtained by linearizing the non-linear constraints of the original problem. Preliminary numerical results show that the proposed solution is capable of effectively limiting the percentage of unsatisfied requests without over-replicating the contents over the CDN servers.

The content delivery networks (CDN) paradigm is based on the idea to transparently move third-party content closer to the users. More specifically, content is replicated on CDN servers which are located close to the final users, and user requests are redirected to the "best" replica (e.g. the closest) in a transparent way, so that users perceive a better content access service. In this paper we address the problem of dynamic replica placement. Being dynamic, our solutions adoptively select the number of replicas for each content and the replicas positions to account for traffic requests dynamics. The schemes we propose are designed to minimize the overall cost paid by the CDN provider (for replicas placement, removal, and maintenance) without degrading the quality of the users perceived access service. The contributions of the paper are twofold. First we introduce a centralized and distributed scheme for replica placement in a dynamic traffic scenario. Then, by means of a simulation based performance evaluation, we assess the effectiveness of the proposed schemes, and compare their performance with static solutions which have been proven to perform well in the literature. Simulation results show that both the two proposed algorithms achieve very good performance, resulting in a significant improvement over the static solutions. Despite relying on local information only, the distributed scheme has comparable performance to the centralized one. Both the two schemes result in low average distance between the users and their serving replicas, in low average number of replicas, in infrequent replicas add and tear down, and in high probability of being able to serve a request.

Summary form only given. We introduce a novel and unified approach to the problems of device discovery and scatternet formation in multihop Bluetooth networks. By means of ns2 extensive simulations we show that our solution is simple to implement, fast, requires low overhead, both for the device discovery and the scatternet formation phases, and leads to better performance over the major approaches so far proposed in the literature.

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).

This paper introduces approximate analytical models to evaluate the performance of end-to-end measurement based connection admission control (EMBAC) mechanisms, devised for the setup of real time flows over the Internet. These mechanisms rely on users probing the current congestion status of their required network path using a succession of probing packets. If the probing rate measured at the end receivers is greater than a certain threshold, users are allowed to switch to a phase of data exchange; otherwise they abort the call setup attempts. In conformance with the differentiated services framework, routers are oblivious to individual flows, and only need to give higher priority to data packets than to probing traffic. Despite the approximations introduced to make the analysis tractable, our model appears to be extremely accurate for a scenario of constant rate connections. Much less accurate, but useful as a possible starting point for future work, is the extension of the model to a scenario of variable rate connections. Simulation results are also presented in the paper to gain additional quantitative insights on the effectiveness of EMBAC to provide support for tight QoS requirements.

The increasing demand for a variety of new Internet services with different and possibly stringent QoS requirements (i.e. Internet telephony, videoconferencing etc.) requires the design of mechanisms to support QoS guarantees. The current solutions proposed in IETF, RSVP (Resource reSerVation Protocol) and differentiated services, though suitable for many applications, may result inefficient to support real-time services on a call basis. The RSVP is not really scalable and requires substantial changes in the Internet architecture, while differentiated services provide guarantees mostly on a static and permanent basis. In this paper we assume an Internet architecture supporting multiple priorities as needed in differentiated services, and introduce the PCP (phantom circuit protocol), a mechanism that provides a guaranteed bandwidth transport service for circuit oriented connections. The PCP includes a fully scalable call admission control (CAC) and operates on a per call basis. Simulation of the protocol performance for CBR (constant bit rate) traffic under various network conditions show the adherence of the mechanism to theoretical expectations.

A myriad of applications are emerging, in which energy conservation is a critical system parameter for communications. Radio frequency identification device (RFID) networks, smart cards, and even mobile computing devices, in general, need to conserve energy. In RFID systems, nodes are small battery-operated inexpensive devices with radio receiving/transmitting and processing capabilities, integrated into the size of an ID card or smaller. These identification devices are designed for extremely low-cost large-scale applications, such that the replacement of batteries is not feasible. This imposes a critical energy constraint on the communications (access) protocols used in these systems, so that the total time a node needs to be active for transmitting or receiving information should be minimized. Among existing protocols, classical random access protocols are not energy conserving, while deterministic protocols lead to unacceptable delays. This paper deals with designing communications protocols with energy constraint, in which the number of time slots in which tags need to be in the active state is minimized, while the access delay meets the applications constraints. We propose three classes of protocols which combine the fairness of random access protocols with low energy requirements.

Mobile communications implies reliance on a self-contained, portable power source, error-prone fading radio channels and subsequent need for acknowledgments. We propose two energy-conserving variants of the selective repeat ARQ protocol, which reduce the number of acknowledgments sent by the mobile node. The protocols are compared to previously described energy-conserving go-back-N ARQ protocols as well as classical approaches and shown to maintain favorable delay and throughput performances.

This paper deals with the design of a communications protocol which operates under an energy constraint, in which the fraction of timeslots in which tags need to be in the active (awake) state is minimized, and the access delay meets the applications constraints. We present a new approach to this problem, a pseudo-random protocol, which combines the fairness from random access protocols with the low energy requirements of classical TDMA. Considering an analytical model as well as simulations of the system behavior for the cases of uniformly and non-uniformly distributed traffic destinations, we show that in addition to fairness, and low energy requirements the proposed protocol provides low access delays.