Concurrent transmissions (CTX) are a state-of-the-art technique for low-power wireless networking, enabling performance unmatched by conventional routing-based approaches. However, their use is notoriously complex, as it hinges on many low-level system aspects distracting developers from the protocol logic. This is exacerbated in ultra-wideband (UWB) radios, recently shown to enable higher CTX performance than IEEE 802.15.4 and BLE, but significantly more energy-hungry, posing new problems to developers. A case in point is the deep sleep mode in popular UWB radios: it yields negligible consumption (50 nA) in periods of inactivity—crucial in real-world deployments—during which, however, clock drifts may disrupt the tight time synchronization required by CTX. Existing systems avoid the complexity of reconciling these conflicting needs via solutions with significantly higher consumption, hampering real-world applicability. We address these challenges with SAMU, a novel framework allowing developers to fully exploit the advantages of CTX over UWB via simple, expressive, and flexible abstractions removing the underlying complexity, embodied in an optimized and efficient runtime support. A distinctive trait of SAMU is the fine-grained control it provides over individual CTX, unlocking design opportunities unavailable in existing frameworks offering only high-level building blocks (e.g., CTX-based floods). We illustrate the power of SAMU by showing how existing CTX protocols can be easily built atop it, and evaluate the resulting performance in a large-scale testbed. Our results confirm that SAMU successfully combines high-level programming with a low-latency, energy-efficient, and reliable runtime support, providing a foundation for the protocol design and real-world deployment of CTX-based systems.

The synergy between the accurate trajectories offered by ultra-wideband (UWB) systems and techniques to extract higher-level mobility patterns is largely unexplored. We study whether staple techniques designed for systems with coarser resolution apply to UWB, investigating quantitatively the quality of the fine-grained analyses enabled by the latter. To this end, we contribute a novel family of metrics suited to the high UWB spatio-temporal resolution and use them to configure and ascertain the quality of representative techniques along several dimensions. We focus on the well-known stop-move pattern and derive our findings from a real museum setting with the use case of capturing visits to exhibits. We acquire UWB trajectories in both controlled ( in vitro ) and uncontrolled ( in vivo ) conditions, along with ground truth. Despite exhibits being very close to each other, our results show that stops near them can be correctly identified and associated in the vast majority of cases and with very small spatio-temporal error. These positive results from real-world experiments, along with our technical contributions, open new opportunities in exploiting UWB for mobility analyses.

Real-time monitoring of visitor behavior in venues such as museums and shops offers valuable insights into visitor preferences, hotspots, and space occupancy. Visitors typically demonstrate interest, by stopping near points of interest (POIs), such as museum exhibits, for brief durations, often on the order of a few seconds, posing challenges for localization technology and stop detection. In this paper, we present StopTracker, a framework supporting the stop-move segmentation of indoor trajectory streams, relying on Ultra-wideband (UWB) localization for the accurate monitoring of visitor stops and preferences. StopTracker is constructed using SPDstreams, an advanced variant of the popular stop-detection technique SPD, and designed to handle evolving stops. Additionally, StopTracker support the dynamic ranking of POIs. By leveraging UWB technology, StopTracker aims to provide accurate and actionable insights into visitor behavior, enhancing both visitor experience and space management.

Wireless networked control systems (WNCS) are at the forefront of academic and industrial efforts, due to the high deployment flexibility and low cost offered by their untethered multihop operation. However, they pose the unavoidable challenge of matching the reliability and latency of wired systems, exacerbated by energy efficiency constraints. Mainstream solutions, in industry and academia alike, largely rely on the routing-based protocol stacks for IEEE 802.15.4 narrowband radios. We identify an alternative to the status quo in the unexplored synergy between concurrent transmissions (CTX) and ultra-wideband (UWB) radios. Low-power wireless stacks based on CTX are known to offer order-of-magnitude improvements w.r.t. mainstream ones in reliability, latency, and energy-efficiency—i.e., the key WNCS requirements above. UWB is very popular in localization applications but rarely considered in multihop networking despite its high data rate and resilience to interference, yielding a beneficial impact on the requirements above. We elicit the potential of this synergy via a novel UWB stack based on a state-of-the-art CTX design. We quantitatively demonstrate its effectiveness in supporting different closed-loop control strategies in a realistic scenario via experiments in a 36-node, 6-hop cyber-physical testbed enabling direct comparison between the original narrowband system and our UWB one. Results show that the UWB stack achieves$10\times $higher reliability and$3\times $lower latency with half the energy consumption, pushing the envelope of low-power networking support for wireless control.

In many low-power wireless systems, a condition occurring at some nodes (e.g., an anomalous sensor sample, an aperiodic packet to transmit, a new joining node) determines whether the entire network should be awake (e.g., to react to the anomaly, deliver the packet, update the node group) or enter sleep. State-of-the-art protocols exploit periodic network-wide floods based on concurrent transmissions (e.g., via Glossy) to establish the global decision quickly, reliably, and efficiently. Still, time is of the essence: the faster the network agrees, the faster it either reacts or enters sleep.

Applications of ultra-wideband (UWB) for distance estimation (ranging) and localization often involve users wearing tags. Unfortunately, the human body causes significant signal attenuation, reducing ranging accuracy. This specific case of non-line-of-sight (NLOS) condition has received little attention in the literature. Further, state-of-the-art techniques tackling generic NLOS are often based on machine learning, limiting their exploitation on embedded devices. We pursue an alternative approach and show that the features offered by the UWB transceiver, largely neglected by the literature, can be directly exploited to reliably detect human occlusions and optimize ranging accordingly. We base our findings on an extensive exper-imental campaign exploring many radio, system, and deployment dimensions in two environments, resulting in practical guidelines immediately available to the designers of UWB-based systems.

Ultra-wideband (UWB) localization enables user tracking with high spatio-temporal resolution, whose exploitation for detecting higher-level mobility patterns is largely unexplored. We study whether i) existing detection techniques, developed for coarser-grained localization, apply also to UWB trajectories, and ii) the quantitative extent to which this enables finer-grained analyses. We focus on the well-known stop-move pattern, and offer a concrete use case of capturing visits in a real museum. We contribute a novel metric suited to the high UWB spatio-temporal resolution and use it to evaluate representative techniques. We deploy a UWB system in a 25×15 m2museum area and base our analysis on 70000+ positions and 200+ ground-truth stops. These are very close in space and time, yet results confirm very accurate spatio-temporal estimation in the vast majority of cases.

Research advances in low-power wireless systems have greatly benefited from the availability of public testbeds. However, none is currently available for the increasingly popular ultra-wideband (UWB) radios enabling communication and localization. We present Cloves, the first public large-scale testbed supporting UWB.

Event-triggered control (ETC) holds the potential to significantly improve the efficiency of wireless networked control systems. Unfortunately, its real-world impact has hitherto been hampered by the lack of a network stack able to transfer its benefits from theory to practice specifically by supporting the latency and reliability requirements of the aperiodic communication ETC induces. This is precisely the contribution of this paper. Our Wireless Control Bus (WCB) exploits carefully orchestrated network-wide floods of concurrent transmissions to minimize overhead during quiescent, steady-state periods, and ensures timely and reliable collection of sensor readings and dissemination of actuation commands when an ETC triggering condition is violated. Using a cyber-physical testbed emulating a water distribution system controlled over a real-world multi-hop wireless network, we show that ETC over WCB achieves the same quality of periodic control at a fraction of the energy costs, therefore unleashing and concretely demonstrating its full potential for the first time.

Devices to support social distancing must be energy-efficient and accurate. Bluetooth Low Energy (BLE) meets the first criteria but falls short on the latter. Ultra-wideband (UWB) measures distances with <10 cm error but with relatively high consumption. Therefore, we built Janus, a dual-radio protocol that uses the strengths of each.

Concurrent transmissions (CTX) enable low latency, high reliability, and energy efficiency. Nevertheless, existing protocols typically exploit CTX via the Glossy system, whose fixed-length network-wide floods are entirely dedicated to disseminating a single packet.

No abstract available.

We propose a novel concurrent ranging technique for distance estimation with ultra-wideband (UWB) radios. Conventional schemes assume that the necessary packet exchanges occur in isolation to avoid collisions. Concurrent ranging relies on the overlapping of replies from nearby responders to the same ranging request issued by an initiator node. As UWB transmissions rely on short pulses, the individual times of arrival can be discriminated by examining the channel impulse response (CIR) of the initiator transceiver. By ranging against N responders with a single , concurrent exchange, our technique drastically abates network overhead, enabling higher ranging frequency with lower latency and energy consumption w.r.t. conventional schemes. Concurrent ranging can be implemented with a strawman approach requiring minimal changes to standard schemes. Nevertheless, we empirically show that this limits the attainable accuracy, reliability, and therefore applicability. We identify the main challenges in realizing concurrent ranging without dedicated hardware and tackle them by contributing several techniques, used in synergy in our prototype based on the popular DW1000 transceiver. Our evaluation, with static targets and a mobile robot, confirms that concurrent ranging reliably achieves decimeter-level distance and position accuracy, comparable to conventional schemes but at a fraction of the network and energy cost.

Modern protocols for wireless sensor networks efficiently support multi-hop upward traffic from many sensors to a collection point, a key functionality enabling monitoring applications. However, the ever-evolving scenarios involving low-power wireless devices increasingly require support also for downward traffic, e.g., enabling a controller to issue actuation commands based on the monitored data. The IETF Routing Protocol for Low-power and Lossy Networks (RPL) is among the few tackling both traffic patterns. Unfortunately, its support for downward traffic is significantly unreliable and inefficient compared to its upward counterpart. We tackle this problem by extending RPL with mechanisms inspired by opposed, yet complementary, principles. At one extreme, we retain the route-based operation of RPL and devise techniques allowed by the standard but commonly neglected by popular implementations. At the other extreme, we rely on flooding as the main networking primitive. Inspired by these principles, we define three base mechanisms, integrate them in a popular RPL implementation, analyze their individual and combined performance, and elicit the resulting tradeoffs in scalability, reliability, and energy consumption. The evaluation relies on simulation, using both real-world topologies from a smart city scenario and synthetic grid ones, as well as on testbed experiments validating our findings from simulation. Results show that the combination of all three mechanisms into a novel protocol, T-RPL (i) yields high reliability, close to the one of flooding, (ii) with a low energy consumption, similar to route-based approaches, and (iii) improves remarkably the scalability of RPL with respect to downward traffic.

Time difference of arrival (TDoA) localization with ultra-wideband (UWB) radios is rapidly gaining interest. Nevertheless, TDoA requires tightly time-synchronized anchors, either via wireless synchronization in small-scale setups with all anchors in range or via expensive wired backbones, both ultimately hampering the use of TDoA in large-scale areas. In this paper we present TALLA, a novel wireless-only TDoA approach able to scale over large operational areas without sacrificing positioning accuracy. TALLA relies on a TDMA schedule enabling continuous, multi-hop operation of the anchor infrastructure. We evaluate TALLA in our 12-node UWB testbed and in much larger (>100 anchors) simulated areas, empowered by a technique that generates synthetic timing information faithfully reproducing the trends of the real one. Our real and simulated results show that TALLA achieves decimeter-level accuracy while tracking a moving target across several hops.

We propose Chorus, a new ultra-wideband (UWB) localization scheme in which the target device computes the time difference of arrival (TDOA) of signals sent concurrently by localization anchors in known positions. This scheme, similar to GPS, is the opposite of existing TDOA schemes for UWB, where the target transmits the signal and anchors compute the time difference. This reversed perspective enables several advantages in Chorus, including support for countless targets.

Many research and industrial communities are betting on LoRa to provide reliable, long-range communication for the Internet of Things. This new radio technology, however, provides widely heterogeneous coverage; a LoRa link may span hundreds of meters or tens of kilometers, depending on the surrounding environment. This high variability is not captured by popular channel models for LoRa, and on-site measurements---a common alternative---are impractical due to the large geographical areas involved.

We consider the problem of accurate and high-rate self-localization for a mobile robot. We adaptively combine the speed information acquired by proprioceptive sensors with intermittent positioning samples acquired via ultra-wideband (UWB) radios. These are triggered only if and when needed to reduce the positioning uncertainty, itself modeled by a probabilistic cost function. Our formulation is agnostic w.r.t. the source of uncertainty and enables an intuitive specification of user navigation requirements along with stochastic guarantees on the system operation. Experimental results in simulation and with a real platform show that our approach i) meets these guarantees in practice ii) achieves the same accuracy of a fixed periodic sampling but with significantly higher scalability and lower energy consumption iii) is resilient to errors in UWB estimates, enabling the use of low-accuracy ranging schemes which further improve these two performance metrics.

Concurrent transmissions can be used to enhance network reliability and scalability and to reduce energy consumption and latency. This paper studies their applicability for communication and ranging in UWB networks, where they are hitherto largely unexplored. To this end, we follow an experiment-driven approach and show that i) different pulse repetition frequencies virtually double the number of non-interfering channels, ii) concurrent transmissions with different preamble codes are unreliable, unless transmitters are tightly synchronized, and iii) under the same RF configuration, UWB radios are very likely to receive one of the packets transmitted concurrently by multiple senders, unlocking opportunities similar to those exploited in low-power narrowband radios. We argue that our findings can inform the design of novel communication and ranging protocols exploiting the unique advantages of concurrent transmissions, potentially inspiring a new wave of research on UWB radios.

The deployment of a wireless sensor network (WSN) is crucial to its reliability and performance. Yet, node placement is typically determined in-field via effort-demanding trial-and-error procedures, because existing approaches over-simplify the radio environment; this especially holds for forests, the focus of this article, where trees greatly affect communication. We present LaPS (LiDAR-assisted Placement for wireless Sensor networks), an approach exploiting remote sensing to identify the best node placement automatically and prior to deployment . Airborne Light Detection and Ranging (LiDAR) data acquired for the target forest are automatically processed to estimate its properties (e.g., tree position and diameter) that, once incorporated into a specialized path loss model, enable per-link estimates of the radio signal attenuation induced by trees. Finally, a genetic algorithm explores placement options by evolving toward a (sub-)optimal solution while satisfying the user’s spatial and network requirements, whose formulation is very flexible and broadly applicable. Our experiments, focused on a real forest, confirm that LaPS yields topologies of significantly higher quality w.r.t. approaches using a regular placement or a standard path loss model. Further, the ability to quickly explore the impact that changes in user requirements have on topology is invaluable to improve the operation of WSNs and reduce the effort of their in-field deployment.

A wide gap exists between the state of the art in developing Wireless Sensor Network (WSN) software and current practices concerning the design, execution, and maintenance of business processes. WSN software is most often developed based on low-level OS abstractions, whereas business process development leverages high-level languages and tools. This state of affairs places WSNs at the fringe of industry. The makeSense system addresses this problem by simplifying the integration of WSNs into business processes. Developers use BPMN models extended with WSN-specific constructs to specify the application behavior across both traditional business process execution environments and the WSN itself, which is to be equipped with application-specific software. We compile these models into a high-level intermediate language-also directly usable by WSN developers-and then into OS-specific deployment-ready binaries. Key to this process is the notion of meta-abstraction, which we define to capture fundamental patterns of interaction with and within the WSN. The concrete realization of meta-abstractions is application-specific; developers tailor the system configuration by selecting concrete abstractions out of the existing codebase or by providing their own. Our evaluation of makeSense shows that i) users perceive our approach as a significant advance over the state of the art, providing evidence of the increased developer productivity when using makeSense; ii) in large-scale simulations, our prototype exhibits an acceptable system overhead and good scaling properties, demonstrating the general applicability of makeSense; and, iii) our prototype-including the complete tool-chain and underlying system support-sustains a real-world deployment where estimates by domain specialists indicate the potential for drastic reductions in the total cost of ownership compared to wired and conventional WSN-based solutions.

Aperiodic data collection received little attention in wireless sensor networks, compared to its periodic counterpart. The recent Crystal system uses synchronous transmissions to support aperiodic traffic with near-perfect reliability, low latency, and ultra-low power consumption. However, its performance is known under mild interference-a concern, as Crystal relies heavily on the (noise-sensitive) capture effect and targets aperiodic traffic where "every packet counts". We exploit a 49-node indoor testbed where, in contrast to existing evaluations using only naturally present interference to evaluate synchronous systems, we rely on JamLab to generate noise patterns that are not only more disruptive and extensive, but also reproducible. We show that a properly configured, unmodified Crystal yields perfect reliability (unlike Glossy) in several noise scenarios, but cannot sustain extreme ones (e.g., an emulated microwave oven near the sink) that instead are handled by routing-based approaches. We extend Crystal with techniques known to mitigate interference-channel hopping and noise detection-and demonstrate that these allow Crystal to achieve performance akin to the original even under multiple sources of strong interference.

Identifying "who is around" is key in a plethora of smart scenarios. While many solutions exist, they often take a theoretical approach, reasoning about protocol behavior with an abstract model that makes simplifying assumptions about the environment. This approach creates a gap between protocol implementations and the models used during design and analysis. In this paper, we take a system approach to continuous neighbor discovery: starting with the concrete technology of Bluetooth Low Energy (BLE) we build a protocol, called BLEnd, tailored to its constraints. Moreover, we also consider the very real effects of packet collisions, to our knowledge a first in this domain. Our ultimate goal is to directly empower developers with the ability to determine the optimal protocol configuration for their applications; in this respect, the slotless operation of BLEnd offers richer alternatives than state-of-the-art protocols. Developers specify the minimum discovery probability, the target discovery latency, and the maximum expected node density; these are used by an optimizer tool to parameterize the BLEnd implementation towards maximum lifetime. This paper shows that BLEnd not only achieves the user-specified goals, but does so more efficiently than analogous configurations of competing protocols.

Directional antennas improve network performance by increasing the communication range and alleviating contention as proven, e.g., in cellular and ad-hoc networks. In principle, one may reap similar benefits in wireless sensor networks (WSNs), where energy concerns and reliability requirements make this antenna technology even more desirable. However, it is unclear how the shortcomings of directional antennas, e.g., increased likelihood of hidden terminals, affect WSNs. We quantitatively study these aspects for convergecast, a staple network functionality popular in WSN applications, e.g., for data collection. The integration of directional communication in convergecast protocols is non-trivial: probing wireless links between neighboring nodes is no longer feasible with single broadcast transmissions, as the antenna configuration depends on the target neighbor. This bears a great impact on the efficiency in building and maintaining the routing topology. We perform our study in simulation, based on an empirical model of an existing antenna prototype. This allows us to explore the parameter space efficiently yet realistically, a goal otherwise impossible without several antenna prototypes that, unlike WSN motes, are not readily available. Our results point to a negative answer, directional antennas, when used for WSN convergecast, provide limited benefits, appreciable only when certain specific conditions are met.

Data prediction in wireless sensor networks replaces the commonly used (periodic) data reporting with a model, updated (infrequently) at the sink to accurately reproduce real data trends. This technique abates up to 99% of application messages; yet, recent work has shown it achieves "only" up to a 7x lifetime improvement when executed atop a mainstream network stack (e.g., CTP + BoX-MAC), as the idle listening and topology maintenance in the latter are ill-suited to the sparse traffic induced by data prediction. This paper presents a novel network stack designed for data prediction, CRYSTAL, that exploits synchronous transmissions to quickly and reliably transmit model updates when these occur (infrequently but often concurrently), and minimizes overhead during the (frequent) periods with no updates. Based on 90-node experiments in the Indriya testbed and with 7 public datasets, we show that CRYSTAL unleashes the full potential of data prediction, achieving per-mille duty cycle with perfect reliability and very small latency.

Wireless sensor networks offer unprecedented opportunities to monitor natural ecosystems. However, despite the growing number of applications (e.g., Forest fire detection, wildlife monitoring), the deployment challenges posed by the real-world natural environment still hinder the widespread adoption of this technology. In particular, the unpredictability of the low-power wireless channel in the presence of vegetation requires costly trial-and-error pilot campaigns to understand where and how to place the wireless nodes. In this paper, we propose a technique based on remote sensing for accurately estimating low-power radio signal attenuation in forest environments. We leverage airborne Light Detection and Ranging (LiDAR) instruments and related automatic data analysis systems to determine local forest attributes (e.g., Tree density) that, once factored into a specialized radio path loss model, enable accurate estimation of the received signal power. Our approach is i) automatic, i.e., It does not require in-field campaigns, and ii) fine-grained, i.e., It enables per-link estimates. Our validation from deployments in a real forest shows that the error of our per-link estimates of the received signal power is around ± 6 dBm - the accuracy of RSSI readings from the radio transceiver.

Existing systems for wildlife monitoring focus either on acquiring the location of animals via GPS or detecting their proximity via wireless communication; the integration of the two, remarkably increasing the biological value of the data gathered, is hitherto unexplored. We offer this integration as our first contribution, embodied by our WildScope system whose key functionality is geo-referenced proximity detection of an animal to others or to landmarks. However, to be truly useful to biologists, the in-field monitoring system must be complemented by two key elements, largely neglected by the literature and constituting our other contributions: i) a model exposing the tradeoffs between accuracy and lifetime, enabling biologists to determine the configuration best suited to their needs, a task complicated by the rich set of on-board devices (GPS, low-power radio, GSM modem) whose activation depends strongly on the biological questions and target species at hand; ii) a validation in controlled experiments that, by eliciting the relationship between proximity detection, the distance at which it reliably occurs, and the location acquisition, provides the cornerstone for the biologists' analysis of wildlife behavior. We test WildScope in real-world experimental setups and deployments with different degrees of control, ascertaining the platform accuracy w.r.t. ground truth and comparing against a commercial proximity logger.

RPL, the IPv6 Routing Protocol for Low-Power and Lossy Networks, supports both upward and downward traffic. The latter is fundamental for actuation, for queries, and for any bidirectional protocol such as TCP, yet its support is compromised by memory limitation in the nodes. In RPL storing mode, nodes store routing entries for each destination in their sub-graph, limiting the size of the network, and often leading to unreachable nodes and protocol failures. We propose here D-RPL, a mechanism that overcomes the scalability limitation by mending storing mode forwarding with multicast-based dissemination. Our modification has minimal impact on code size and memory usage. D-RPL is activated only when memory limits are reached, and affects only the portion of the traffic and the segments of the network that have exceeded memory limits. We evaluate our solution using Cooja emulation over different synthetic topologies, showing a six-fold improvement in scalability.

Data prediction is proposed in wireless sensor networks (WSNs) to extend the system lifetime by enabling the sink to determine the data sampled, within some accuracy bounds, with only minimal communication from source nodes. Several theoretical studies clearly demonstrate the tremendous potential of this approach, able to suppress the vast majority of data reports at the source nodes. Nevertheless, the techniques employed are relatively complex, and their feasibility on resource-scarce WSN devices is often not ascertained. More generally, the literature lacks reports from real-world deployments, quantifying the overall system-wide lifetime improvements determined by the interplay of data prediction with the underlying network. These two aspects, feasibility and system-wide gains, are key in determining the practical usefulness of data prediction in real-world WSN applications. In this paper, we describe derivative-based prediction (DBP), a novel data prediction technique much simpler than those found in the literature. Evaluation with real data sets from diverse WSN deployments shows that DBP often performs better than the competition, with data suppression rates up to 99 percent and good prediction accuracy. However, experiments with a real WSN in a road tunnel show that, when the network stack is taken into consideration, DBP only triples lifetime-a remarkable result per se, but a far cry from the data suppression rates above. To fully achieve the energy savings enabled by data prediction, the data and network layers must be jointly optimized. In our testbed experiments, a simple tuning of the MAC and routing stack, taking into account the operation of DBP, yields a remarkable seven-fold lifetime improvement w.r.t. the mainstream periodic reporting.

Wireless sensor networks (WSNs) enable decentralized architectures to monitor the behavior of physical processes and to detect deviations from a specified “safe” behavior, for example, to check the operation of control loops. Such correct behavior is typically expressed by global invariants over the state of different sensors or actuators. Nevertheless, to leverage the computing capabilities of WSN nodes, the application intelligence needs to reside inside the network. The task of ensuring that the monitored processes behave safely thus becomes inherently distributed, and hence more complex. In this article we present DICE, a system enabling WSN-based distributed monitoring of global invariants. A DICE invariant is expressed by predicates defined over the state of multiple WSN nodes, such as the expected state of actuators based on given sensed environmental conditions. Our modular design allows two alternative protocols for detecting invariant violations: both perform in-network aggregation but with different degrees of decentralization, therefore supporting scenarios with different network and data dynamics. We characterize and compare the two protocols using large-scale simulations and a real-world testbed. Our results indicate that invariant violations are detected in a timely and energy-efficient manner. For instance, in a 225-node 15-hop network, invariant violations are detected in less than a second and with only a few packets sent by each node.

How do the characteristics of the surrounding environment affect the ability of the nodes of a wireless sensor network (WSN) to communicate? Partial answers to this question can be found in the literature, but always with a focus on the short-term, small-scale behavior of individual links, as this directly informs the design of WSN protocols. In this paper, we are instead concerned with the large scale behavior of the overall network, observed over a longer time scale, as our primary interest is to support the deployment of WSNs by characterizing the impact of the target environment. Motivated by a real-world wildlife monitoring application, we report about experimental campaigns in three outdoor environments characterized by varying degrees of vegetation. Experiments are repeated in summer and winter, to account for seasonal variations, and span multiple days, allowing us to assess variations induced by the succession of day and night. Our experiments focus primarily on characterizing the impact of the environment on the physical layer, but we also investigate how this is mirrored at higher layers. We analyze the experimental data along multiple dimensions, yielding quantitative answers to the aforementioned question, and eliciting trends and findings previously not reported in the literature. We argue that this type of study may inspire new methods to better estimate the performance of a WSN in its target deployment environment.

Decentralized social networks are an emerging solution to the privacy issues plaguing mainstream centralized architectures. Social overlays-overlay networks mirroring the social relationships among node owners-are particularly intriguing, as they limit communication within one's friend circle. Previous work investigated efficient protocols for P2P dissemination in social overlays, but also showed that the churn induced by users, combined with the topology constraints posed by these overlays, may yield unacceptable latency. In this paper, we combine P2P dissemination on the social overlay with occasional access to the cloud. When updates from a friend are not received for a long time, the cloud serves as an external channel to verify their presence. The outcome is disseminated in a P2P fashion, quenching cloud access from other nodes and speeding dissemination of existing updates. We show that our protocol performs close to centralized architectures and incurs only modest monetary costs.

We study the benefits of electronically-switched directional (ESD) antennas in wireless sensor networks (WSNs). ESD antennas have proved beneficial in cellular and ad-hoc networks, by increasing the communication range and by alleviating contention in directions other than the destination. The advantages in WSNs are, however, still largely to be quantified. Unlike existing works in this field, we start by characterizing a real-world antenna prototype, and apply this to an existing WSN stack, which we adapt with minimal changes. Our results show that: i) the combination of a low-cost ESD antenna and a mainstream WSN stack already brings significant performance improvements, e.g., nearly halving the radio-on time per delivered packet; ii) the margin of improvement available to alternative clean-slate protocol designs is similarly large and concentrated in the control rather than the data plane; iii) by artificially modifying our antenna's link-layer model, further potential benefits opened by different antenna designs may be available. To the best of our knowledge, this is the first study providing such quantitative insights based on a real ESD antenna prototype and a complete WSN stack.

We study what happens when the same wireless sensor network (WSN) is "immersed" in different outdoor environments, namely, two types of forests and an open field. Qualitative answers can be derived from existing results. Here, we aim to quantify the extent of changes, both at the physical and application layer, based on the combined effect of environmental parameters over daily and seasonal time-scales.

Time series forecasting aims at improving energy efficiency in wireless sensor networks (WSNs) by reducing the amount of data traffic. One such technique has each node generate a model that predicts the sampled data. When the actual, sensed data deviates from the model, a new model is generated and transmitted to the sink. Reductions in application data traffic as high as two orders of magnitude can be achieved. However, our experience in applying such forecasting in a real world deployment shows that the actual lifetime improvement is significantly less due to networking overheads. The study reported here reveals that careful, coordinated network parameter tuning can leverage the reduced traffic of forecasting techniques to increase lifetime without compromising application performance.

The industrial adoption of wireless sensor networks (WSNs) is hampered by two main factors. First, there is a lack of integration of WSNs with business process modeling languages and back-ends. Second, programming WSNs is still challenging as it is mainly performed at the operating system level. To this end, we provide makeSense: a unified programming framework and a compilation chain that, from high-level business process specifications, generates code ready for deployment on WSN nodes.

Peer-to-peer systems based on an overlay network that mirrors the social relationships among the nodes' owners are increasingly attracting interest. Yet, the churn induced by the availability of users raises the question-still unanswered-of whether these social overlays represent a viable solution. Indeed, although constraining communication to take place only among “friends” brings many benefits, it also introduces significant limitations when healing the overlay in the presence of churn. This paper puts forth two contributions. First, we show through simulation on real datasets that churn induces relevant delays in information dissemination, which may ultimately hamper the practical application of social overlays. Yet, identifying opportunities for improvement and evaluating design alternatives through simulation is impractical, due to the size of the target networks, the large parameter space, and the many sources of randomness involved. Therefore, in our second contribution we combine analytical and simulation techniques to enable the estimation of dissemination delays at a practical cost.

Model-driven data acquisition techniques aim at reducing the amount of data reported, and therefore the energy consumed, in wireless sensor networks (WSNs). At each node, a model predicts the sampled data; when the latter deviate from the current model, a new model is generated and sent to the data sink. However, experiences in real-world deployments have not been reported in the literature. Evaluation typically focuses solely on the quantity of data reports suppressed at source nodes: the interplay between data modeling and the underlying network protocols is not analyzed. In contrast, this paper investigates in practice whether i) model-driven data acquisition works in a real application; ii) the energy savings it enables in theory are still worthwhile once the network stack is taken into account. We do so in the concrete setting of a WSN-based system for adaptive lighting in road tunnels. Our novel modeling technique, Derivative-Based Prediction (DBP), suppresses up to 99% of the data reports, while meeting the error tolerance of our application. DBP is considerably simpler than competing techniques, yet performs better in our real setting. Experiments in both an indoor testbed and an operational road tunnel show also that, once the network stack is taken into consideration, DBP triples the WSN lifetime-a remarkable result per se, but a far cry from the aforementioned 99% data suppression. This suggests that, to fully exploit the energy savings enabled by data modeling techniques, a coordinated operation of the data and network layers is necessary.

We describe the motivation, focus, and organization of SESENA11, the 2nd International Workshop on Software Engineering for Sensor Network Applications. The workshop took place under the umbrella of ICSE 2011, the 33rd ACM/IEEE International Conference on Software Engineering, in Honolulu, Hawaii, on May 22, 2011. The aim was to attract researchers belonging to the Software Engineering (SE) and Wireless Sensor Network (WSN) communities, not only to exchange their recent research results on the topic, but also to stimulate discussion on the core open problems and define a shared research agenda. More information can be found at the workshop website: http://www.sesena.info.

Online social networks (OSN) have attracted millions of users worldwide. This enormous success is not without problems; the centralized architectures of OSNs, storing the users' personal data, provides ample opportunity for privacy violation - a fact that has raised the demand for open, decentralized alternatives. We tackle the research question: is it possible to build a decentralized OSN over a social overlay, i.e., an overlay network whose links among nodes mirror the social network relationships among the nodes' owners? This paper provides a stepping stone to the answer, by focusing on the key OSN functionality of disseminating profile updates. Our approach relies on gossip protocols. We show that mainstream gossip protocols are inefficient, due to the properties that characterize social networks. We then leverage these very same properties towards our goal, by appropriately modifying gossip forwarding rules. Our evaluation, performed in simulation over a crawled real-world social network, shows that our protocols provide acceptable latency, foster load balancing across nodes, and tolerate churn.

Assessing the connectivity of Wireless Sensor Networks in the specific environment in which they are deployed is crucial to develop reliable system services and understand their behavior. In this demo, we introduce Trident, a tool that measures communication with an untethered infrastructure. It enables the execution of connectivity experiments "in the wild", supporting also the sharing of the gathered results.

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Wireless sensor networks (WSNs) are envisioned for a number of application scenarios. Nevertheless, the few in-the-field experiences typically focus on the features of a specific system, and rarely report about the characteristics of the target environment, especially with respect to the behavior and performance of low-power wireless communication. The TRITon project, funded by our local administration, aims to improve safety and reduce maintenance costs of road tunnels, using a WSN-based control infrastructure. The access to real tunnels within TRITon gives us the opportunity to experimentally assess the peculiarities of this environment, hitherto not investigated in the WSN field. We report about three deployments: (i) an operational road tunnel, enabling us to assess the impact of vehicular traffic; (ii) a nonoperational tunnel, providing insights into analogous scenarios (e.g., underground mines) without vehicles; (iii) a vineyard, serving as a baseline representative of the existing literature. Our setup, replicated in each deployment, uses mainstream WSN hardware, and popular MAC and routing protocols. We analyze and compare the deployments with respect to reliability, stability, and asymmetry of links, the accuracy of link quality estimators, and the impact of these aspects on MAC and routing layers. Our analysis shows that a number of criteria commonly used in the design of WSN protocols do not hold in tunnels. Therefore, our results are useful for designing networking solutions operating efficiently in similar environments.

Content-based routing (CBR) is becoming increasingly popular as a building block for distributed applications. CBR differs from classical routing paradigms as messages are routed based on their content rather than their destination address, which fosters decoupling and flexibility in the application's distributed architecture. However, most available systems realize CBR by relying on a tree-shaped overlay network and adopt a routing strategy based on broadcasting subscription requests, thus hampering applicability in very large-scale networks. We observe that a fundamental underpinning of any CBR protocol is for messages and subscriptions to "meet" at some points in the network. In the approach we propose here, called HyperCBR1, we enforce this topological property in a multidimensional space, by routing messages and subscriptions on different, albeit intersecting, partitions. We derive an analytical model of HyperCBR, validated through simulation, and use it to evaluate our approach in two relevant CBR contexts - content-based searches in peer-to-peer networks, and content- based publish-subscribe. The results show that our protocol achieves efficient CBR even in very large scale settings (e.g., millions of nodes) while at the same time opening up intriguing opportunities for deployment-time tuning based on the expected traffic profiles. The analytical evaluation is complemented by simulation results relying on a CAN-based implementation, showing that HyperCBR generates a small forwarding and matching load, and that it is able to tolerate high churn with low overhead.

Applications involving the dissemination of information directly relevant to humans (e.g., service advertising, news spreading, environmental alerts) often rely on publish-subscribe, in which the network delivers a published message only to the nodes whose subscribed interests match it. In principle, publish- subscribe is particularly useful in mobile environments, since it minimizes the coupling among communication parties. However, to the best of our knowledge, none of the (few) works that tackled publish-subscribe in mobile environments has yet addressed intermittently-connected human networks. Socially-related people tend to be co-located quite regularly. This characteristic can be exploited to drive forwarding decisions in the interest-based routing layer supporting the publish-subscribe network, yielding not only improved performance but also the ability to overcome high rates of mobility and long-lasting disconnections. In this paper we propose SocialCast, a routing framework for publish-subscribe that exploits predictions based on metrics of social interaction (e.g., patterns of movements among communities) to identify the best information carriers. We highlight the principles underlying our protocol, illustrate its operation, and evaluate its performance using a mobility model based on a social network validated with real human mobility traces. The evaluation shows that prediction of colocation and node mobility allow for maintaining a very high and steady event delivery with low overhead and latency, despite the variation in density, number of replicas per message or speed.

Content-based routing (CBR) provides a powerful and flexible foundation for distributed applications. Its communication model, based on implicit addressing, fosters decoupling among the communicating components, therefore meeting the needs of many dynamic scenarios, including mobile ad hoc networks (MANETs). Unfortunately, the characteristics of the CBR model are only rarely met by available systems, which typically assume that application-level routers are organized in a tree-shaped network with a fixed topology. In this paper, we present COMAN, a protocol to organize the nodes of a MANET in a tree-shaped network able to 1) self- repair to tolerate the frequent topological reconfigurations typical of MANETs and 2) achieve this goal through repair strategies that minimize the changes that may impact the CBR layer exploiting the tree. COMAN is implemented and publicly available. Here, we report about its performance in simulated scenarios, as well as in real-world experiments. The results confirm that its characteristics enable reliable and efficient CBR on MANETs.

Wireless sensor networks are increasingly employed to develop sophisticated applications where heterogeneous nodes are deployed, and multiple parallel activities must be performed. Therefore, application developers require the ability to partition the system based on the node characteristics, and specify complex interactions among different partitions. Existing programming abstractions for sensor networks tackled this problem by providing a notion of scoping. However, this rarely emerges as a first-class programming construct, hence limiting its applicability. To address this issue, in this paper we present a flexible notion of scoping in the context of a sensor network macroprogramming framework. Our approach enables the specification of complex interactions among system partitions, thus greatly simplifying the development process. Moreover, this is not detrimental to performance: our approach results reasonably close to an optimal solution computed with global system knowledge, while exhibiting a 70% gain w.r.t. baseline solutions.

Due to the inherent nature of their heterogeneity, resource scarcity and dynamism, the provision of middleware for future networked embedded environments is a challenging task. In this paper we present a middleware approach that addresses these key challenges; we also discuss its application in a realistic networked embedded environment. Our application scenario involves fire management in a road tunnel that is instrumented with networked sensor and actuator devices. These devices are able to reconfigure their behaviour and their information dissemination strategies as they become damaged under emergency conditions, and firefighters are able to coordinate their operations and manage sensors and actuators through dynamic reprogramming. Our supporting middleware is based on a two-level architecture: the foundation is a language-independent, component-based programming model that is sufficiently minimal to run on any of the devices typically found in networked embedded environments. Above this is a layer of software components that offer the necessary middleware functionality. Rather than providing a monolithic middleware 'layer', we separate orthogonal areas of middleware functionality into self-contained components that can be selectively and individually deployed according to current resource constraints and application needs. Crucially, the set of such components can be updated at runtime to provide the basis of a highly dynamic and reconfigurable system

Wireless sensor networks (WSNs) involving actuation are increasingly envisioned in a range of fields. For instance, there is considerable interest in leveraging off WSNs to improve safety in road tunnels [3]. Researchers are envisioning tunnels equipped with WSN nodes that gather physical readings (e.g., light), monitor the structural integrity of the tunnel, and sense the presence of vehicles to detect a possible traffic congestion. Based on sensed data, the system operates a variety of devices, such as ventilation fans inside the tunnel, and traffic lights at the entrances. For instance, when a sensor detects the presence of a fire in a sector, the fans in the same sector are activated, and the traffic lights are turned red to prevent further vehicles from entering the tunnel.

Next generation embedded systems will be composed of large numbers of heterogeneous devices. These will typically be resource-constrained, will use different operating systems, and will be connected through different types of network interfaces. Additionally, they may be mobile and/or form ad-hoc networks with their peers, and will need to be adaptive to changing conditions based on context-awareness. As an example of these system we consider disaster recovery scenarios where large numbers of different devices need to interconnect in an ad-hoc manner. In this respect, our goal is the provisioning of a middleware framework for such system environments. Our approach is based on a small and efficient middleware kernel supporting highly modularised and customisable component-based middleware services. These services can be tailored for specific embedded environments, and are runtime-reconfigurable to support adaptivity. This paper describes a demonstration that highlights some of the features available in our middleware. In particular, we focus on heterogeneity handling by showing our middleware running on resource-rich as well as resource-constrained devices, and on adaptivity features by demonstrating runtime reprogramming and on-the-fly component deployment

LIME (Linda in a mobile environment) is a model and middleware supporting the development of applications that exhibit the physical mobility of hosts, logical mobility of agents, or both. LIME adopts a coordination perspective inspired by work on the Linda model. The context for computation, represented in Linda by a globally accessible persistent tuple space, is refined in LIME to transient sharing of the identically named tuple spaces carried by individual mobile units. Tuple spaces are also extended with a notion of location and programs are given the ability to react to specified states. The resulting model provides a minimalist set of abstractions that facilitates the rapid and dependable development of mobile applications. In this article we illustrate the model underlying LIME, provide a formal semantic characterization for the operations it makes available to the application developer, present its current design and implementation, and discuss lessons learned in developing applications that involve physical mobility.

Mainstream approaches to content-based distributed publish-subscribe typically route events deterministically based on information collected from subscribers, and do so by relying on a tree-shaped overlay network. While this solution achieves scalability in fixed, large-scale settings, it is less appealing in scenarios characterized by high dynamicity, e.g., mobile ad hoc networks or peer-to-peer systems. At the other extreme, researchers in the related fields of multicast and group communication have successfully exploited probabilistic techniques that provide increased fault tolerance, resilience to changes, and yet are scalable. In this paper, we propose a novel approach where event routing relies on deterministic decisions driven by a limited view on the subscription information and, when this is not sufficient, resorts to probabilistic decisions performed by selecting links at random. Simulations show that the particular mix of deterministic and probabilistic decisions we put forth in this work is very effective at providing high event delivery and low overhead in highly dynamic scenarios, without sacrificing scalability

Mobile code solutions can improve the performances of applications over the Internet by reducing the amount of data that must be transferred across the network. The research on genome examines enormous amount of data stored on many distributed databases to retrieve the information relevant for the specific investigation, and thus is likely to greatly benefit for mobile code solutions. This paper studies the benefits of mobile code solutions for research on genome, by comparing different solutions with analytical models, and presenting the results of case studies that allow to identify when mobile code overwhelm client/server solutions. The measured improvements of performance indicate that the retrieval of information from genome databases is a promising domain for mobile code applications.

In this paper we propose a routing strategy for enabling publish-subscribe communication in a sensor network. The approach is semi-probabilistic, in that it relies partly on the dissemination of subscription information and, in the areas where this is not available, on random rebroadcast of event messages. We illustrate the details of our approach, concisely describe its implementation in TinyOS (J.Hill et al., 2000) for the MICA2 platform, and evaluate its performance through simulation. Results show that our approach provides good delivery and low overhead, and is resilient to connectivity changes in the sensor network, as induced by the temporary standby necessary to preserve the energy of sensor nodes

In the rapidly developing field of sensor networks, bridging the gap between the applications and the hardware presents a major challenge. Although middleware is one solution, it must be specialized to the qualities of sensor networks, especially energy consumption. The work presented here provides two contributions: a new operational setting for sensor networks and a middleware for easing software development in this setting. The operational setting we target removes the usual assumption of a central collection point for sensor data. Instead the sensors are sparsely distributed in an environment, not necessarily able to communicate among themselves, and a set of clients move through space accessing the data of sensors nearby, yielding a system which naturally provides context relevant information to client applications. We further assume the clients are wirelessly networked and share locally accessed data. This scenario is relevant, for example, when relief workers access the information in their zone and share this information with other workers. Our second contribution, the middleware itself is an extension of LlME, our earlier work on middleware for mobile ad hoc networks. The model makes sensor data available through a tuple space interface, providing the illusion of shared memory between applications and sensors. This paper presents both the model and the implementation of our middleware incorporated with the Crossbow Mote sensor platform.

In this paper the RUNES approach to the development of software for networked embedded systems is described. There is a need for a program platform with abstractions that are able to span the full range of heterogeneous embedded systems, and which also offers consistent mechanisms with which to configure, deploy, and dynamically reconfigure networked embedded systems software. This paper discusses the need of such a programming platform. The work is being carried out in the context of the RUNES project (reconfigurable, ubiquitous, and networked embedded systems) which has the general goal of developing an architecture for networked embedded systems that encompasses dedicated radio layers, networks

Distributed content-based publish-subscribe middleware is emerging as a promising answer to the demands of modern distributed computing. Nevertheless, currently available systems usually do not provide reliability guarantees. This hampers their use in dynamic and unreliable scenarios, notably including mobile ones. We evaluate the effectiveness of an approach based on epidemic algorithms. Three algorithms we originally proposed in [P. Costa et al., (2003)] are thoroughly compared and evaluated through simulation in challenging unreliable settings. The results show that our use of epidemic algorithms improves significantly event delivery, is scalable, and introduces only limited overhead.

Distributed content-based publish-subscribe middleware provides the decoupling, flexibility, expressiveness, and scalability required by highly dynamic distributed applications, e.g., mobile ones. Nevertheless, the available systems exploiting a distributed event dispatcher are unable to rearrange dynamically their behavior to adapt to changes in the topology of the dispatching infrastructure. In this work, we first define a strawman solution based on ideas proposed (but never precisely characterized) in existing work. We then analyze this solution and achieve a deeper understanding of how the event dispatching information is reconfigured. Based on this analysis, we modify the strawman approach to reduce its overhead. Simulations show that the reduction is significant (up to 50%), and yet the algorithm is resilient to concurrent reconfigurations.

LIME is a middleware supporting the development of applications that exhibit physical mobility of hosts, logical mobility of agents, or both. LIME adapts a coordination perspective inspired by work on the Linda model. The context for computation, represented in Linda by a globally accessible, persistent tuple space, is represented in LIME by transient sharing of the tuple spaces carried by each individual mobile unit. Linda tuple spaces are also extended with a notion of location and with the ability to react to a given state. The hypothesis underlying our work is that the resulting model provides a minimalist set of abstractions that enable rapid and dependable development of mobile applications. In this paper, we illustrate the model underlying LIME, present its current design and implementation, and discuss initial lessons learned in developing applications that involve physical mobility.

Advancements in network technology have led to the emergence of new computing paradigms that challenge established programming practices by employing weak forms of consistency and dynamic forms of binding. Code mobility, for instance, allows for invocation-time binding between a code fragment and the location where it executes. Similarly, mobile computing allows hosts (and the software they execute) to alter their physical location. Despite apparent similarities, the two paradigms are distinct in their treatment of location and movement. This paper seeks to uncover a common foundation for the two paradigms by exploring the manner in which stereotypical forms of code mobility can be expressed in a programming notation developed for mobile computing. Several solutions to a distributed simulation problem are used to illustrate the modeling strategy and the ability to construct assertional-style proofs for programs that employ code mobility.

The tutorial provides a conceptual framework for code mobility by illustrating a taxonomy of related technologies, architectural paradigms, and applications. As a final case study, the concepts developed in the taxonomy are then applied to a quantitative assessment of the benefits of mobile code technologies and architectures in the network management application domain.

Mobile computing defines a very dynamic and challenging scenario for which software engineering practices are still largely in their initial developments. LIME is a middleware designed to enable the rapid development of dependable applications in the mobile environment. The model underlying LIME allows for coordination of physical and logical mobile units by exploiting a reactive, transiently shared tuple space whose contents changes according to connectivity. In this demonstration, we report about initial experiences in developing applications for physical mobility using LIME.

Article LIME: Linda meets mobility Share on Authors: Gian Pietro Picco Dept. of Computer Science, Washington University, Campus Box 1045, One Brookings Drive, St. Louis, MO Dept. of Computer Science, Washington University, Campus Box 1045, One Brookings Drive, St. Louis, MOView Profile , Amy L. Murphy Dept. of Computer Science, Washington University, Campus Box 1045, One Brookings Drive, St. Louis, MO Dept. of Computer Science, Washington University, Campus Box 1045, One Brookings Drive, St. Louis, MOView Profile , Gruia-Catalin Roman Dept. of Computer Science, Washington University, Campus Box 1045, One Brookings Drive, St. Louis, MO Dept. of Computer Science, Washington University, Campus Box 1045, One Brookings Drive, St. Louis, MOView Profile Authors Info & Claims ICSE '99: Proceedings of the 21st international conference on Software engineeringMay 1999 Pages 368–377https://doi.org/10.1145/302405.302659Online:16 May 1999Publication History 239citation755DownloadsMetricsTotal Citations239Total Downloads755Last 12 Months30Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The question of whether technologies supporting mobile code are bringing significant benefits to the design and implementation of distributed applications is still an open one. Even more difficult is to identify precisely under which conditions a design exploiting mobile code is preferable over a traditional one. In this work, we present an in-depth evaluation of several mobile code design paradigms against the traditional client-server architecture, within the application domain of network management. The evaluation is centered around a quantitative model, which is used to determine precisely the conditions for the selection of a design paradigm minimizing the network traffic related to management.

Software processes are complex entities that demand careful understand ing and improvement as they determine the quality of the resulting product. A necessary step toward the improvement of an organization's process is a clear description of the entities involved and of their mutual relationships. Process modelelicitationaims at constructing this description under the shape of a software process model. The model is constructed by gathering, from several sources, process information which is often incomplete, inconsistent, and ambiguous. A process modeling language can be used to represent the model being elicited. However, elicitation requires process models to be understandable and well structured. These requirements are often not satisfied by available process modeling languages becuase of their bias toward process enaction rather than process description. This article presents a process modeling language and a support tool which are conceived especially for process model elicitation. TheE3language is an object-oriented modeling language with a graphical notation. InE3, associations are a means to express constraints and facilitate reuse. TheE3p-draw tool supports the creation and management ofE3models and provides a view mechanism that enables inspection of models according to different perspectives.

The technologies, architectures, and methodologies traditionally used to develop distributed applications exhibit a variety of limitations and drawbacks when applied to large scale distributed settings (e.g., the Internet). In particular, they fail in providing the desired degree of configurability, scalability, and customizability. To address these issues, researchers are investigating a variety of innovative approaches. The most promising and intriguing ones are those based on the ability of moving code across the nodes of a network, exploiting the notion of mobile code. As an emerging research field, code mobility is generating a growing body of scientific literature and industrial developments. Nevertheless, the field is still characterized by the lack of a sound and comprehensive body of concepts and terms. As a consequence, it is rather difficult to understand, assess, and compare the existing approaches. In turn, this limits our ability to fully exploit them in practice, and to further promote the research work on mobile code. Indeed, a significant symptom of this situation is the lack of a commonly accepted and sound definition of the term mobile code itself. This paper presents a conceptual framework for understanding code mobility. The framework is centered around a classification that introduces three dimensions: technologies, design paradigms, and applications. The contribution of the paper is two-fold. First, it provides a set of terms and concepts to understand and compare the approaches based on the notion of mobile code. Second, it introduces criteria and guidelines that support the developer in the identification of the classes of applications that can leverage off of mobile code, in the design of these applications, and, finally, in the selection of the most appropriate implementation technologies. The presentation of the classification is intertwined with a review of state-of-the-art in the field. Finally, the use of the classification is exemplified in a case study.

Large scale distributed systems are becoming of paramount importance, due to the evolution of technology and to the interest of market.Their development, however, is not yet supported by a sound technological and methodological background, as the results developed for small size distributed systems often do not scale up.Recently, mobile code languages (MCLs) have been proposed as a technological answer to the problem.In this work, we abstract away from the details of these languages by deriving design paradigms exploiting code mobility that are independent of any particular technology.We present such design paradigms, together with a discussion of their features, their application domain, and some hints about the selection of the correct paradigm for a given distributed application.

The paper discusses the problems that a software development organization must address in order to assess and improve its software processes. In particular, the authors are involved in a project aiming at assessing and improving the current practice and the quality manual of the Business Unit Telecommunications for Defense (BUTD) of a large telecommunications company. The paper reports on the usage of formal process modeling languages to detect inconsistencies, ambiguities, incompleteness, and opportunities for improvement of both the software process and its documentation.>