Mobile Extended Reality (XR) communication services offer unparalleled opportunities across various verticals, but present specific challenges due to their high throughput requirements, often in the hundreds of Mbps, and stringent end-to-end delay constraints, usually as low as a few milliseconds. To address these challenges, we propose the eXtended Reality-Oriented Orchestration of Access Resources (X-OAR), designed to support XR applications, by leveraging next-generation network access technologies, even in densely populated environments. X-OAR integrates cooperatively scheduled radio access network resources with edge computing capabilities. X-OAR complies with the stringent delay requirements defined by 3GPP for XR quality of experience through cooperative joint edge and radio resources scheduling. We formulate the delay minimization problem using a graph-based approach and introduce a greedy algorithm that reduces orchestration complexity and prior knowledge of user subscription data. Numerical simulations demonstrate that X-OAR’s cooperative scheduling outperforms state-of-the-art solutions, delivering superior XR quality of experience. Furthermore, X-OAR paves the way for future research on extending orchestration to application-layer strategies and resource-aware charging policies.

The unfolding climate crisis has resulted in a rising interest for increasing sustainability awareness and achieving energy savings worldwide. Several interventions within educational environments have been aimed at mobilizing younger audiences toward such goals. However, most of the interventions carried out so far are based on a subset of possible tools (e.g., IoT monitoring and gamification) and offer “all or nothing” approaches, which do not cope well with the uniqueness of schools and the need to adapt interventions to specific contexts (e.g. school size, building, curriculum, and teacher involvement). We present a large-scale intervention in 25 school buildings in Europe over the course of two school years. Our intervention was based on the installation of a relatively low-cost IoT infrastructure in schools, that produced real-time energy-related data from the school buildings involved, and the application of a strategy combining, in a flexible and customizable way, educational activities, monitoring tools, gamification, and competition to motivate behavior change and achieve energy savings. Overall, 2983 students and 204 educators were directly involved to a variable degree of participation. Our results indicate a positive overall result in terms of short-term energy savings in most schools involved, with an average reduction of 20%–25% in the energy that could be affected by end-users, as well as a 32.9% increase after the intervention in students self-reporting good or very good sustainability awareness. These findings suggest that such interventions can be a valuable step toward educating young students regarding sustainability and toward sustainable schools, if designed and implemented properly.

Abstract Touristic experience (TE) is a unique and underexplored category of digital tourism products. It is multi-modal, dynamic, and highly subjective, which poses significant challenges for traditional tourism recommender systems. To address these challenges, we present an Intelligent Embedded Recommender Network (IERN), a framework specifically designed for sparse user interaction data on digital tourism platforms (DTPs), where conventional tourism recommendation systems often fail to perform effectively. Our approach is based on advanced user profiling through extensive feature transformations, deep neural profile learners, intelligent embeddings, and clusters with associative similarity. The framework presents a ranked list of TE recommendations to users with predicted ratings and sentiments. For enhanced usability, our framework augments recommendations with contextual features: weather forecasts, live traffic updates, and multi-modal interaction tools —including chatbot and voice recommendations. Comparative evaluations with baseline models reveal that our model shows decreased MSE by percentages 38.00%, 61.03%, and 19.48% for Airbnb, TripAdvisor, and Booking.com datasets, respectively. It also outperforms baseline models for MAE, MAAPE, and R $$^2$$ parameters. In addition to utility error measures, our framework demonstrates increased diversity by 0.53%, 0.55%, and 0.35%, and exhibits better item space coverage by 30.12%, 55.02%, and 42.93% for Airbnb, TripAdvisor, and Booking.com datasets, respectively. This study advances digital tourism with practical implications, specifically for sparse-interaction data scenarios.

LoRaWAN is a wireless technology that enables high-density deployments of IoT devices. Designed for Low Power Wide Area Networks (LPWAN), LoRaWAN employs large cells to service a potentially extremely high number of devices. The technology enforces a centralized architecture, directing all data generated by the devices to a single network server for data processing. End-to-end encryption is used to guarantee the confidentiality and security of data. In this demo, we present Edge2LoRa, a system architecture designed to incorporate edge processing in LoRaWAN without compromising the security and confidentiality of data. Edge2LoRa maintains backward compatibility and addresses scalability issues arising from handling large amounts of data sourced from a diverse range of devices. The demo provides evidence of the advantages in terms of reduced latency, lower network bandwidth requirements, higher scalability, and improved security and privacy resulting from the application of the Edge processing paradigm to LoRaWAN.

International migration forces people into an unfamiliar reality in which their customs and values lose relevance. Moreover, former relationships are left behind, which makes immigrants more likely to experience loneliness. This study focuses particularly on Venezuelan immigrants by incorporating cultural aspects into a solution aimed at reducing loneliness and increasing social connectedness. Among Venezuelans, coffee is a staple of their daily routine and their favorite social beverage. We propose KEPEIN, a coffee maker-shaped interface to transfer a sense of presence and share coffee over distance. Through an experimental study, we evaluated the user’s perception and reaction when communicating through the interface. The results show potential added value to communication by including KEPEIN in a traditional remote interaction scenario. We discuss the benefits and limitations of this type of tangible communication interface and the importance of incorporating culture into the design of solutions for immigrants.

The optimization and digitalization of Water Distribution Networks (WDNs) are becoming key objectives in our modern society. Indeed, WDNs are typically old, worn and obsolete. These inadequate conditions of the infrastructures lead to significant water loss due to leakages inside pipes, junctions and nodes. It has been measured that in Europe the average value of lost water is about 26 %. Leakage control in current WDNs is typically passive, repairing leaks only when they are visible. Emerging Low Power Wide Area Network (LPWAN) technologies, and especially IoT ones, can help monitor water consumption and automatically detect leakages. In this context, LoRaWAN can be the right way to deploy a smart monitoring system for WDNs. Moreover, most of the current smart WDNs solutions just collect measurements from the smart metres and send the data to the cloud servers, in order to execute the intended analyses, in centralised way. In this paper, we propose new solutions to improve monitoring, leak management and prediction by exploiting edge processing capabilities inside LoRaWAN networks. Our approach is based on an IoT system of water sensors that are placed at junctions of the WDN to have measurements in correspondence to various smart metres in the network and Machine Learning (ML) algorithms to process the data directly at the edge in order to visualise and predict leakages. We present a numerical simulation tool useful to evaluate the suggested monitoring method. Based on our results, we examine whether it is possible to identify network leaks using the edges without having a complete or accurate overview of the collected measurements of the full WDN. System performance is shown separately at gateways network.

Adopting modern emerging technologies of smart metering based on IoT that extend existing water distribution systems is of paramount importance for improving the operational efficiency of the water supply system in the cities and rural communities. Real-world data collected by smart water meters allows the analysis of consumption of water. In this work, data collected over a period of 28 months is analyzed on weekly, daily and hourly basis to identify usage patterns in relation to the restrictions imposed by the local authorities as a response to the COVID-19 emergency taking place during 2020 and 2021. The evaluation of water consumption before, during and after the lockdown periods highlights the impact of human actions. A novel data-driven method is presented to profile buildings that can help identify similarities and highlight differences across water usage patterns.

We present here an IoT-based platform that provides an integrated solution for real-time monitoring and management of educational buildings at a national scale. The proposed system follows the Fog Computing paradigm so that sensor data processing takes place at the edge devices of the network. In this way, the system significantly reduces the network traffic across the network core layers. The architecture and implementation of the system are presented in details in relation to existing use-case scenaria. The performance of the prototype architecture is evaluated in a real-world environment using a range of edge devices available in a pilot deployment spanning across 18 school buildings. The evaluation indicates that existing resources are sufficient to accommodate traffic that can increase up to 5 times higher from the existing one even in sites where low-end devices (e.g., such as Raspberry Pi) are available. The results provide evidence that Fog Computing can address the ever-increasing amount of data that is inherent in an IoT world by effective communication among all elements of the architecture.

Heart disease and stroke are becoming the leading causes of death worldwide. Electrocardiography monitoring devices (ECG) are the only tool that help physicians diagnose cardiac abnormalities. Although the design of ECGs has followed closely the electronics miniaturization evolution over the years, existing wearable ECGs have limited accuracy and rely on external resources to analyse the signals and evaluate heart activity. In this paper, we work towards empowering the wearable device with processing capabilities to locally analyse the signal and identify abnormal behaviour. The ability to differentiate between normal and abnormal heart activity significantly reduces (a) the need to store the signals, (b) the data transmitted to the cloud, (c) the overall power consumption and (d) the confidentiality of private data. Based on this concept, the HEART system presented in this work, combines wearable embedded devices, mobile edge devices, and cloud services to provide on-the-spot, reliable, accurate, and instant heart monitoring. The wearable device is remotely trained by a physician to learn to accurately identify critical events related to each particular patient. Following this training session, the wearable device becomes capable of interpreting a large number of heart abnormalities without relying on cloud services and edge resources, when the medical doctor is not present. The Fog computing approach extends the cloud computing paradigm by migrating data-processing closer to production site, thus accelerating the system's responsiveness to events. The HEART system's performance concerning the accuracy of detecting abnormal events and the power consumption of the wearable device is evaluated. Results indicate that a very high success rate can be achieved in terms of event detection ratio and the battery is able to sustain operation up to a full week without the need for recharge.

Fog computing is considered as the most promising enhancement of the traditional cloud computing paradigm in order to handle potential issues introduced by the emerging Interned of Things (IoT) framework at the network edge. The heterogeneous nature, the extensive distribution and the hefty number of deployed IoT nodes will disrupt existing functional models, creating confusion. However, IoT will facilitate the rise of new applications, with automated healthcare monitoring platforms being amongst them. This paper presents the pillars of design for such applications, along with the evaluation of a working prototype that collects ECG traces from a tailor-made device and utilizes the patient's smartphone as a Fog gateway for securely sharing them to other authorized entities. This prototype will allow patients to share information to their physicians, monitor their health status independently and notify the authorities rapidly in emergency situations. Historical data will also be available for further analysis, towards identifying patterns that may improve medical diagnoses in the foreseeable future.

The world of machine-to-machine (M2M) communication is gradually moving from vertical single purpose solutions to multi-purpose and collaborative applications interacting across industry verticals, organizations and people - a world of Internet of Things (IoT). The dominant approach for delivering IoT applications relies on the development of cloud-based IoT platforms that collect all the data generated by the sensing elements and centrally process the information to create real business value. In this paper, we present a system that follows the Fog Computing paradigm where the sensor resources, as well as the intermediate layers between embedded devices and cloud computing datacenters, participate by providing computational, storage, and control. We discuss the design aspects of our system and present a pilot deployment for the evaluating the performance in a real-world environment. Our findings indicate that Fog Computing can address the ever-increasing amount of data that is inherent in an IoT world by effective communication among all elements of the architecture.

Short lived chest pain episodes of post PCI patients represent the most common clinical scenario treated in the Accidents and Emergency Room. Continuous ECG monitoring could substantially diminish such hospital admissions and related ambulance calls. Delivering community based, easy-to-handle, easy to wear, real time electrocardiography systems is still a quest, despite the existence of electronic electrocardiography systems for several decades. The PATRIOT system serves this challenge via a 12-channel, easy to wear, easy to carry, mobile linked, miniaturized automatic ECG device and a Cloud platform. The system may deliver high quality electrocardiograms of a patient to medical personnel either on the spot or remotely both in a synchronous or asynchronous mode, enhancing autonomy, mobility, quality of life and safety of recently treated coronary artery disease patients.

Topology Control Protocols configure transmission power of nodes in order to achieve specific properties to a given topology. These properties include the creation and maintenance of neighborhoods or other topological entities (like trees or clusters), load balancing in terms of connectivity degrees and provision of link symmetry. We see topology control as a two-fold problem where topological properties can also be affected by local network throughput. We propose SCLD-A2TP, a protocol that operates in a two phase adaptive scheme. First transmission power is adaptively adjusted with low throughput settings and nodes achieve a sufficient degree of symmetric and coherent links. Secondly throughput is maximized insofar as the degree is maintained. We assess various distributed heuristics for SCLD-A2TP via test bed experiments and show that up to an extend, link quality and symmetry as well as degree conformity of links can be regulated successfully by transmission power and adaptive throughput control.

In this paper we investigate the problem of counting the size of a network where processes are anonymous (i.e., they share the same identifier) and the network topology constantly changes controlled by an adversary able to look internal process states and add and remove edges in order to contrast the convergence of the algorithm to the correct count. It is easy to show that, if the adversary can generate graphs without any constraint on the connectivity (i.e. it can generate topologies where there exist nodes not able to influence the others), counting is impossible. In this paper we consider a synchronous round based computation and the dynamicity is governed by a worst-case adversary that generates a sequence of graphs, one for each round, with the only constraint that each graph must be connected (1-interval connectivity property). It has been conjectured that counting in a finite time against such adversary is impossible and the existing solutions consider that each process has some knowledge about network topologies generated by the adversary, i.e. at each round, each node has a degree lesser than D. Along the path of proving the validity (or not) of the conjecture, this paper presents an algorithm that counts in a finite time against the worst-case adversary assuming each process is equipped with an oracle. The latter provides a process at each round r with an estimation of the process degree in the graph generated by the adversary at round r. To the best of our knowledge, this is the first counting algorithm (terminating in a finite time) where processes exploit the minimal knowledge about the behavior of the adversary. Interestingly, such oracle can be implemented in a wide range of real systems.

Modern Wireless Sensor Networks offer an easy, low-cost and reliable alternative to the back-end for monitoring and controlling large geographical areas like Buildings and Industries. We present the design and implementation details of an open and efficient Prototype System as a solution for low-cost BMS that comprises of heterogeneous, small-factor wireless devices. Placing that in the context of Internet of Things we come up with a solution that can cooperate with other systems installed on the same site to lower power consumption and costs as well as benefit humans that use its services in an transparent way. We evaluate and assess key aspects of the performance of our prototype. Our findings indicate specific approaches to reduce the operation costs and allow the development of open applications.

In this paper, we discuss the integration of Wireless Sensor Networks (WSN) and smart objects with the Web. We present a set of research challenges which we believe are the most important ones rising from this integration and propose a prototype system, Überdust, which addresses such challenges. Überdust is a brokerage web service for connecting smart objects to the Internet of Things, providing storage, sharing and discovery of real-time and historical data from smart objects, devices & building installations around the world via the Web. Our system provides high-level language-independent APIs so IoT application developers may choose their favorite programming or scripting languages.

Wireless Sensor Networks are by nature highly dynamic and communication between sensors is completely ad hoc, especially when mobile devices are part of the setup. Numerous protocols and applications proposed for such networks operate on the assumption that knowledge of the neighborhood is a priori available to all nodes. As a result, WSN deployments need to use or implement from scratch a neighborhood discovery mechanism. In this work we present a new protocol based on adaptive periodic beacon exchanges. We totally avoid continuous beaconing by adjusting the rate of broadcasts using the concept of consistency over the understanding of neighborhood that nearby devices share. We propose, implement and evaluate our adaptive neighborhood discovery protocol over our experimental testbed and using large scale simulations. Our results indicate that the new protocol operates more efficiently than existing reference implementations while it provides valid information to applications that use it. Extensive performance evaluation indicates that it successfully reduces generated network traffic by 90% and increases network lifetime by 20% compared to existing mechanisms that rely on continuous beaconing.

Evaluating target tracking protocols for wireless sensor networks that can localize multiple mobile devices, can be a very challenging task. Such protocols usually aim at minimizing communication overhead, data processing for the participating nodes, as well as delivering adequate tracking information of the mobile targets in a timely manner. In this paper we design a new localization protocol, where mobile assets can be tracked passively via software agents. We address the issues that hinder its performance due to the real environment conditions and provide a deployable protocol. The implementation, integration and experimentation of this new protocol and it's optimizations, were performed using the WISEBED framework. We apply our protocol in an indoors wireless sensor testbeds with multiple experimental scenarios to showcase scalability and trade-offs between network properties and configurable protocol parameters. The analysis of experimental results, depict a real view of the target tracking problem, regarding power consumption and the quality of tracking information.

As the Internet of Things (IOT) arises, the use of low-end devices on a daily basis increases. The wireless nature of communication that these devices provide raises security and privacy issues. For protecting a user's privacy, cryptography offers the tool of zero knowledge proofs (ZKP). In this paper, we study well-established ZKP protocols based on the discrete logarithm problem and we adapt them to the Elliptic Curve Cryptography (ECC) setting, which consists an ideal candidate for embedded implementations. Then, we implement the proposed protocols on Wiselib, a generic and open source algorithmic library. For the first time, we present a thorough evaluation of the protocols on two popular hardware platforms equipped with low end microcontrollers (Jennic JN5139, TI MSP430) and 802.15.4 RF transceivers, in terms of code size, execution time, message size and energy requirements. This work's results can be used from developers who wish to achieve certain levels of privacy in their applications.

We propose a new theoretical model for passively mobile wireless sensor networks, called P M , standing for passively mobile machines . The main modification w.r.t. the population protocol model (Angluin et al., 2006) [30] is that agents now, instead of being automata, are Turing Machines. We provide general definitions for unbounded memories, but we are mainly interested in computations upper-bounded by plausible space limitations. However, we prove that our results hold for more general cases. We focus on complete interaction graphs and define the complexity classes PMSPACE ( f ( n ) ) parametrically, consisting of all predicates that are stably computable by some PM protocol that uses O ( f ( n ) ) memory in each agent. We provide a protocol that generates unique identifiers from scratch only by using O ( log n ) memory, and use it to provide an exact characterization of the classes PMSPACE ( f ( n ) ) when f ( n ) = Ω ( log n ) : they are precisely the classes of all symmetric predicates in NSPACE ( n f ( n ) ) . As a consequence, we obtain a space hierarchy of the PM model when the memory bounds are Ω ( log n ) . We next explore the computability of the PM model when the protocols use o ( log log n ) space per machine and prove that SEM = PMSPACE ( f ( n ) ) when f ( n ) = o ( log log n ) , where SEM denotes the class of the semilinear predicates. Finally, we establish that the minimal space requirement for the computation of non-semilinear predicates is O ( log log n ) .

We examine multi-player pervasive games that rely on the use of ad-hoc mobile sensor networks. The unique feature in such games is that players interact with each other and their surrounding environment by using movement and presence as a means of performing game-related actions, utilizing sensor devices. We briefly discuss the fundamental issues and challenges related to these type of games and the scenarios associated with them. We have also developed a framework, called Fun in Numbers (FinN) that handles a number of these issues, such as such as neighbors discovery, localization, synchronization and delay-tolerant communication. FinN is developed using Java and is based on a multilayer architecture, which provides developers with a set of templates and services for building and operating new games

In this paper we present a platform for developing mobile, locative and collaborative distributed games comprised of small programmable object technologies (e.g., wireless sensor networks) and traditional networked processors. The platform is implemented using a combination of JAVA Standard and Mobile editions, targeting also mobile phones that have some kind of sensors installed. We briefly present the architecture of our platform and demonstrate its capabilities by reporting two pervasive multiplayer games. The key characteristic of these games is that players interact with each other and their surrounding environment by moving, running and gesturing as a means to perform game related actions, using small programmable object technologies.

In this work we study the problem of scheduling tasks with dependencies in multiprocessor architectures where processors have different speeds. We examine the energy-efficiency and time efficiency of scheduling in an asymmetric system.

In this work we present two mobile, locative and collaborative distributed games that are played using wireless sensor devices. We briefly present the architecture of the two games and demonstrate their capabilities. The key characteristic of these games is that players interact with each other and their surrounding environment by moving, running and gesturing as a means to perform game related actions, using sensor devices. We demonstrate our system's implementation, which uses a combination of JAVA Standard and Mobile editions.

In this paper, we survey the current state-of-the-art in middleware and systems for Wireless Sensor Networks (WSN). We provide a discussion on the definition ofWSN middleware, design issues associated with it, and the taxonomies commonly used to categorize it. We also present a categorization of a number of such middleware platforms, using middleware functionalities and challenges which we think will play a crucial role in developing software for WSN in the near future. Finally, we provide a short discussion on WSN middleware trends.

Large-scale sensor networks, monitoring an environment at close range with high spatial and temporal resolutions are expected to play an important role in various applications, e.g., assessing the "health" of machines; environmental, medical, food-safety, and habitat monitoring; inventory control, building automation, etc. Ensuring the security of these complex and yet resource-constrained systems has emerged as one of the most pressing challenges for researchers. In this paper (i) we present the major threats, and (ii) we present a new approach for decentralized energy efficient intrusion detection that can be used to improve security from both external and internal adversaries.

Wireless sensor networks are comprised of a vast number of ultra-small autonomous computing, communication and sensing devices, with restricted energy and computing capabilities, that co-operate to accomplish a large sensing task. Such networks can be very useful in practice, e.g. in the local monitoring of ambient conditions and reporting them to a control center. In this paper we propose a distributed group key establishment protocol that uses mobile agents (software) and is particularly suitable for energy constrained, dynamically evolving ad-hoc networks. Our approach totally avoids the construction and the maintenance of a distributed structure that reflects the topology of the network. Moreover, it trades-off complex message exchanges by performing some amount of additional local computations in order to be applicable at dense and dynamic sensor networks. The extra computations are simple for the devices to implement and are evenly distributed across the participants of the network leading to good energy balance. We evaluate the performance of our protocol in a simulated environment and compare our results with existing group key establishment protocols. The security of the protocol is based on the Diffie-Hellman problem and we used in our experiments its elliptic curve analog. Our findings basically indicate the feasibility of implementing our protocol in real sensor network devices and highlight the advantages and disadvantages of each approach given the available technology and the corresponding efficiency (energy, time) criteria.

In ad-hoc mobile networks (MANET), the mobility of the nodes is a complicating factor that significantly affects the effectiveness and performance of the routing protocols. Our work builds upon the recent results on the effect of node mobility on the performance of available routing strategies (i.e. path based, using support) and proposes a protocol framework that exploits the usually different mobility rates of the nodes by adopting the routing strategy during execution. We introduce a metric for the relative mobility of the nodes, according to which the nodes are classified into mobility classes. These mobility classes determine, for any pair of origin and destination, the routing technique that best corresponds to their mobility properties. Moreover, special care is taken for nodes remaining almost stationary or moving with high (relative) speeds. Our key design goal is to limit the necessery implementation changes required to incorporate existing routing protocols in our framework. We provide extensive evaluation of the proposed framework, using a well-known simulator (NS2). Our first findings demonstrate that the proposed framework improves, in certain cases, the performance of the existing routing protocols.

We introduce a new modelling assumption in wireless sensor networks, that of node redeployment (addition of sensor devices during the protocol evolution) and we extend the modelling assumption of heterogeneity (having sensor devices of various types). These two features further increase the highly dynamic nature of such networks and adaptation becomes a powerful technique for protocol design. Under this model, we design, implement and evaluate a power conservation scheme for efficient data propagation. Our protocol is adaptive: it locally monitors the network conditions (density, energy) and accordingly adjusts the sleep-awake schedules of the nodes towards best operation choices. Our protocol operates does not require exchange of control messages between nodes to coordinate.Implementing our protocol we combine it with two well-known data propagation protocols and evaluate the achieved performance through a detailed simulation study using our extended version of Ns2. We focus in highly dynamic scenarios with respect to network density, traffic conditions and sensor node resources. We propose a new general and parameterized metric capturing the trade-off between delivery rate, energy efficiency and latency. The simulation findings demonstrate significant gains (such as more than doubling the success rate of the well-known Directed Diffusion propagation paradigm) and good trade-offs. Furthermore, redeployment of sensors during network evolution and/or heterogeneous deployment of sensors drastically improve (when compared to equal total "power" simultaneous deployment of identical sensors at the start) the protocol performance (the success rate increases up to four times while reducing energy dissipation and, interestingly, keeping latency low).

Wireless sensor networks are comprised of a vast number of ultra-small fully autonomous computing, communication and sensing devices, with very restricted energy and computing capabilities, which co-operate to accomplish a large sensing task. Such networks can be very useful in practice in applications that require fine-grain monitoring of physical environment subjected to critical conditions (such as inaccessible terrains or disaster places). Features including the huge number of sensor devices involved, the severe power, computational and memory limitations, their dense deployment and frequent failures, pose new design and implementation aspects. The efficient and robust realization of such large, highly-dynamic, complex, non-conventional, environments is a challenging algorithmic task.

Abstract In this paper, we demonstrate the significant impact of (a) the mobility rate and (b) the user density on the performance of routing protocols in ad‐hoc mobile networks. In particular, we study the effect of these parameters on two different approaches for designing routing protocols: (a) the route creation and maintenance approach and (b) the ‘support’ approach that forces few hosts to move, acting as ‘helpers’ for message delivery. We study one representative protocol for each approach, i.e. AODV for the first approach and RUNNERS for the second. We have implemented the two protocols and performed a large scale and detailed simulation study of their performance. The main findings are: the AODV protocol behaves well in networks of high user density and low mobility rate, while its performance drops for sparse networks of highly mobile users. On the other hand, the RUNNERS protocol seems to tolerate well (and in fact benefit from) high mobility rates and low densities. Copyright © 2004 John Wiley & Sons, Ltd.

We introduce a new model of ad-hoc mobile networks, which we call hierarchical, that are comprised of dense subnetworks of mobile users (corresponding to highly populated geographical areas, such as cities), interconnected across access ports by sparse but frequently used connections (such as highways). For such networks, we present an efficient routing protocol which extends the idea (introduced in [4]) of exploiting the co-ordinated motion of a small part of an ad-hoc mobile network (the "support") to achieve very fast communication between any two mobile users of the network. The basic idea of the new protocol presented here is, instead of using a unique (large) support for the whole network, to employ a hierarchy of (small) supports (one for each city) and also take advantage of the regular traffic of mobile users across the interconnection highways to communicate between cities. We combine here theoretical analysis (average case estimations based on random walk properties) and experimental implementations (carried out using the LEDA platform) to claim and validate results showing that such a hierarchical routing approach is, for this class of ad-hoc mobile networks, significantly more efficient than a simple extension of the basic "support" idea presented in [4].

Article An efficient communication strategy for ad-hoc mobile networks Share on Authors: Ioannis Chatzigiannakis Computer Technology Institute, Patras, Greece Computer Technology Institute, Patras, GreeceView Profile , Sotiris Nikoletseas Computer Technology Institute, Patras, Greece Computer Technology Institute, Patras, GreeceView Profile , Paul Spirakis Computer Technology Institute, Patras, Greece Computer Technology Institute, Patras, GreeceView Profile Authors Info & Claims PODC '01: Proceedings of the twentieth annual ACM symposium on Principles of distributed computingAugust 2001 Pages 320–322https://doi.org/10.1145/383962.384053Published:01 August 2001 34citation438DownloadsMetricsTotal Citations34Total Downloads438Last 12 Months1Last 6 weeks1 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