The rise of IoT in the past few years has led to the massive deployment of connected devices making IoT networks denser. To optimize transmission arising from congestion in dense networks, adaptive data-rate algorithms have been implemented such as the one used in the LoRaWAN protocol. Utilization of algorithms based on reinforcement learning, especially multi-armed bandit, have been investigated, but the duty-cycle limitation decreases the performance of these algorithms in dense networks up to 15 %. This paper aims at giving a solution to resolve the issue caused by duty-cycle limitation, using the LoRa technology as a study case. An effort was done on energy consumption and the reward is modified in order to save energy according to the quality of service. Performances are evaluated thanks to our new simulator J-LoRaNeS based on the Julia language.

Managing complex distributed applications in the Cloud-Edge continuum, including deployment on diverse resources and runtime operations, presents significant challenges. Orchestrators play a key role by automating resource discovery, optimisation, deployment, and life-cycle management while ensuring system performance. This paper introduces Swarmchestrate, a decentralised, application-centric orchestration framework inspired by self-organising Swarms. Our initial findings, based on the implementation in a Cloud-Edge simulator, demonstrate Swarmchestrate's potential, offering insights into resource coordination and optimised allocation for scalable systems.

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.

Technology increasingly offers parents more and more opportunities to monitor children, reshaping the way control and autonomy are negotiated within families. This paper investigates the views of parents and primary school children on mobile technology designed to support child independent mobility in the context of the local walking school buses. Based on a school-year long field study, we report findings on children's and parents' experience with proximity detection devices. The results provide insights into how the parents and children accepted and socially appropriated the technology into the walking school bus activity, shedding light on the way they understand and conceptualize a technology that collects data on children's proximity to the volunteers' smartphone. We discuss parents' needs and concerns toward monitoring technologies and the related challenges in terms of trust-control balance. These insights are elaborated to inform the future design of technology for child independent mobility.

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.

Low-power and long-range communication technologies such as LoRa are becoming popular in IoT applications due to their ability to cover kilometers range with milliwatt of power consumption. One of the major drawbacks of LoRa is the data latency and the traffic congestion when the number of devices in the network increases. Especially, the latency arises due to the extreme duty cycling of LoRa end-nodes for reducing the overall energy consumption. To overcome this drawback, we propose a heterogeneous network architecture and an energy-efficient On-demand TDMA communication scheme improving both the device lifetime and the data latency of standard LoRa networks. We combine the capabilities of micro-watt wake-up receivers to achieve ultra-low power states and pure asynchronous communication together with the long-range connectivity of LoRa. Experimental results show a data reliability of 100 % and a round-trip latency on the order of milliseconds with end devices dissipating less than 46 mJ when active and 1.83 μW during periods of inactivity, lasting up to 3 years on a 1200 mA h Lithium battery.

Long-range (LoRa) radio technologies have recently gained momentum in the IoT landscape, allowing low-power communications over distances up to several kilometers. As a result, more and more LoRa networks are being deployed. However, commercially available LoRa devices are expensive and propriety, creating a barrier to entry and possibly slowing down developments and deployments of novel applications. Using open-source hardware and software platforms would allow more developers to test and build intelligent devices resulting in a better overall development ecosystem, lower barriers to entry, and rapid growth in the number of IoT applications. Toward this goal, this paper presents the design, implementation, and evaluation of KRATOS, a low-cost LoRa platform running ContikiOS. Both, our hardware and software designs are released as an open-source to the research community.

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.

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.

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.

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.

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.

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.

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.

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.

LPL [5], or BoX-MAC in its TinyOS implementation, is arguably the most common MAC protocol for WSNs. Its extensive use in real world deployments is justified by a simple implementation, available online, that meets the requirements of a vast majority of monitoring applications. Despite the plethora of competitors, it still remains the reference in the MAC scheduling world. However, its random nature inherently hampers its possibility to support effectively any application driven Quality of Service requirement.

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.

Clustering and aggregation inherently increase wireless sensor network (WSN) lifetime by collecting information within a cluster at a cluster head, reducing the amount of data through computation, then forwarding it. Traditional approaches, however, both spend extensive communication energy to identify the cluster heads and are inflexible to network dynamics such as those arising from sink mobility, node failure, or dwindling battery reserves. This paper presents CLIQUE, an approach for data clustering that saves cluster head selection energy by using machine learning to enable nodes to independently decide whether or not to act as a cluster head on a per-packet basis. We refer to this lack of actual cluster head assignment as being role-free, and demonstrate through simulations that, when combined with learning dynamic network properties such as battery reserves, up to 25% less energy is consumed in comparison to a traditional, random cluster head selection approach.

In October 2004 the University of Lugano in southern Switzerland established a new faculty of informatics. Its founding principles are innovation in teaching and faculty participation in the research community. With respect to teaching, students spend mornings attending lectures and afternoons in an Atelier designed to support interaction both among students and with the instructors. In teaching the first year "Programming Fundamentals" courses, we took advantage of the clean slate nature of the faculty to introduce innovative teaching elements. The novel aspects include our use of Scheme, Smalltalk, and Java, our combination of individual, pair and group projects and the integration of expert lectures to introduce useful, but slightly orthogonal elements at key points in the semester. Our very positive experience is reported along with a discussion of aspects to improve in the future.

Trees are fundamental structures for data dissemination in large-scale network scenarios. However, their inherent fragility has led researchers to rely on more redundant mesh topologies in the presence of churn or other highly dynamic settings. In this paper, instead, we outline a novel protocol that directly and efficiently maintains a tree overlay in the presence of churn. It simultaneously achieves other beneficial properties such as limiting the maximum node degree, minimizing the extent of the tree topology changes resulting from failures, and limiting the number of nodes affected by each topology change. Applicability to a range of distributed applications is discussed and results are evaluated through extensive simulation and a PlanetLab deployment.

Efficient multi-hop data dissemination is a crucial building block to enable mature wireless sensor network (WSN) applications. Exploiting machine learning for these routing problems has received increasing attention in recent years due to its flexibility and localized mechanisms. However, with such an approach the resulting protocols often have additional memory and processing time requirements. Nevertheless, these requirements \emph {are} within the reach of today's WSN hardware, however few substantial tests have been performed to clearly demonstrate this. This paper evaluates and discusses the results and experiences gained from implementing our reinforcement learning based multicast routing protocol (FROMS) in a testbed of ScatterWeb nodes. A comparison of our results is made to a well-known WSN routing scheme, namely a multicast variation of Directed Diffusion. Our evaluation includes several minor, but practical modifications to both protocols such as transmission backoffs and the use of acknowledgments.This paper offers three main contributions. First, we demonstrate that machine learning algorithms \emph{can be} efficiently implemented on resource restricted devices and that they perform very well in multiple network scenarios. Second, we confirm the validity of simulation results obtained in a previous evaluation of FROMS, and at the same time gather delivery rates under realistic settings. Finally, we offer some general observations on properties and pitfalls of WSN implementations along with potential solutions.

Efficiently moving sensor data from its collection to use points is both the fundamental and the most difficult challenge in wireless sensor networks, as any data movement incurs cost. In this work, we focus on routing data to multiple, possibly mobile sinks. To deal with the dynamics of the environment arising from mobility and failures, we choose a reinforcement learning approach where neighboring nodes exchange small amounts of information allowing them to learn the next, best hop to reach all sinks. Preliminary evaluation demonstrates that our technique results in low cost routes with low overhead for the learning process.

In-network data aggregation is widely recognized as an acceptable means to reduce the amount of transmitted data without adversely affecting the quality of the results. To date, most aggregation protocols assume that data from localized regions is correlated, thus they tend to identify aggregation points within these regions. Our work, instead, targets systems where the data sources are largely independent, and over time, the sink requests different combinations of data sources. The combinations are essentially aggregation functions. This problem is significantly different from the localized one because the functions are initially known only by the sink, and the data sources to be combined may be located in any part of the network, not necessarily near one another. This paper describes MVSink, a protocol that lowers the network cost by incrementally pushing the aggregation function as close to the sources as possible, aggregating early the raw data. Our results show significant savings over a simplistic approach, and demonstrate that a data request needs to be active only for a reasonably short period of time to overcome the cost of identifying the best aggregation point.

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.

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.

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.

Future smart environments are characterized by multiple nodes that sense, collect, and disseminate information about environmental phenomena through a wireless network. In this paper, we define a set of applications that require a new form of distributed knowledge about the environment, referred to as non-uniform information granularity. By non-uniform information granularity we mean that the required accuracy or precision of information is proportional to the distance between a source node (information producer) and current sink node (information consumer). That is, as the distance between the source node and sink node increases, loss in information precision is acceptable. Applications that can benefit from this type of knowledge range from battlefield scenarios to rescue operations. The main objectives of this paper are two-fold: first, we precisely define non-uniform information granularity, and second we describe the different protocols that achieve non-uniform information dissemination and analyze these protocols based on complexity, energy consumption, and accuracy of information.

As computing components get smaller and people become accustomed to having computational power at their disposal at any time, mobile computing is developing as an important research area. One of the fundamental problems in mobility is maintaining connectivity through message passing as the user moves through the network. An approach to this is to have a single home node constantly track the current location of the mobile unit and forward messages to this location. One problem with this approach is that, during the update to the home agent after movement, messages are often dropped, especially in the case of frequent movement. In this paper, we present a new algorithm which uses a home agent, but maintains information regarding a subnet within which the mobile unit must be present. We also present a reliable message delivery algorithm which is superimposed on the region maintenance algorithm. Our strategy is based on ideas from diffusing computations as first proposed by Dijkstra and Scholten. Finally, we present a second algorithm which limits the size of the subnet by keeping only a path from the home node to the mobile unit.

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.

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.

No abstract available.

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

With recent advances in wireless communication and the ubiquity of laptops, mobile computing has become an important research area. An essential problem in mobile computing is the delivery of a message from a source to either a single mobile node, unicast, or to a group of mobile nodes, multicast. Standard solutions used in Mobile IP and cellular phones for the unicast problem rely on tracking the mobile unit. Tracking solutions scale badly when mobile nodes move frequently, and do not generalize well to multicast delivery. Our paper proposes a new message delivery algorithm for micromobility based on a modification of classical snapshot algorithms and includes a proof outline using the UNITY logic. Our algorithm requires no tracking, provides stronger guarantees than existing protocols in micromobility, and generalizes easily to multicasting. Besides a particular solution to the delivery problem, our approach offers a new strategy for transferring established results from distributed computing to mobile computing. The general idea is to treat mobile nodes as messages that roam across the fixed network structure and to leverage off existing distributed algorithms that compute information about messages.