The growing scale and complexity of Internet of Things (IoT) networks present critical challenges for maintaining and recovering connectivity, especially in dynamic environments or after large-scale fragmentation caused by disasters. Traditional centralized approaches often face scalability, resilience, and energy efficiency issues. Hyperbolic geometry, which has successfully modeled hierarchical and scale-free structures in large-scale systems such as the Internet's AS topology, offers a promising foundation for addressing these challenges. This paper introduces hyperbolic trees as a novel framework for IoT connectivity management. Nodes are embedded in a hyperbolic plane, and connectivity is maintained or restored through a simple local rule: each node connects to the nearest node with a smaller radial coordinate. This distributed, asynchronous approach allows nodes to autonomously reorganize and recover connectivity without centralized coordination, ensuring scalability and adaptability. We demonstrate that hyperbolic trees provide a robust, energy-efficient solution for both maintaining and recovering IoT network connectivity. Numerical results validate the framework's scalability and effectiveness, highlighting its applicability in scenarios ranging from routine management to disaster recovery. These findings establish hyperbolic trees as a practical and scalable tool for resilient IoT connectivity management in next-generation networks.

The Internet of Things is changing today from a daring vision to reality. Sensors, smartphones, smart vehicles are all around, transforming the physical world into an intelligent environment able to provide context-aware services. To support this, we must propose communication protocols that consider the special requirements of IoT. The 6LoWPAN and RPL protocols were defined for this purpose, enabling IoT devices with scarce resources to get an IPv6 address and connect to the Internet. However, the basic principles of communication have to be adapted also. Instead of using the address of a sensor, we query IoT devices based on their context. Also, we need information from the environment, no matter which IoT device provides it. This is the governing principle of data-centric communication, an area that gets increasing attention and fits perfectly the IoT. Here we propose CAEsAR, a Context-Aware Addressing and Routing scheme. It uses RPL trees and aggregates context information in Bloom-filters along the tree. We compare CAEsAR with an IoT service discovery protocol called TRENDY, and the data-centric protocol called MQTT-SN. Simulations show that CAEsAR generates much less signaling traffic, both in the RPL tree setup phase and when the system adapts to changes of context parameters.

Sleep-scheduling mechanisms are often used in wireless sensor networks (WSN), as they can save a significant amount of energy by turning off the redundant nodes in the network. In a recent survey about energy-efficient scheduling mechanism in WSNs it was however shown that there are virtually no protocols that support a mobile environment. In this paper we propose thus an adaptive energy-efficient sleep scheduling mechanism that is able to handle a dynamic environment, mobile nodes and moving events. The proposed solution is fully distributed, can cope with frequent node failures, and balances energy consumptions, which can significantly extend the global network lifetime. The solution is adaptive, since it can dynamically detect and learn the existence of a linear relation between sensor measurements, eliminate the redundancy, and estimate the deficient data based on the learned relations. The extrapolation error can be well controlled by fine-tuning the threshold of expected inaccuracy, which is a user-specified parameter. We compare our solution with deterministic clustering approaches, provide parameter sensitivity analysis and discuss the simulation results.

Since the emergence of the earliest wireless sensor networks (WSNs), a great deal of effort has been made to support target tracking applications. Although target tracking, or other services such as dynamic sleep scheduling or early warning systems require some level of event forecasting, there was little research on this topic, in spite of its importance. In this paper we formalize a framework for event forecasting based on fuzzy logic and propose an adaptive and scalable algorithm to predict events in the network. Moreover, for each forecast our solution provides a bounded confidence level based on learned time-space fuzzy signatures (TSS). We evaluate the proposed method by means of simulations and provide parameter sensitivity analysis as well.

The increased traffic density in urban areas should be handled by a robust collision avoidance and traffic control system that is based on decentralized inter-vehicle communication. In such a special ad hoc network traditional routing protocols cannot cope with the high mobility and low link reliability, while flooding based approaches consume too many resources. Our proposed solution, Carefully Localized Urban Dissemination (CLoUD) is a location-aided gossiping protocol, which concentrates information spreading to areas where it is most likely to be useful. To assess the efficiency of our protocol, we provide a theoretical analysis and show some simulation results.

Resource information systems are a key component of Computational Grids. Centralized information systems hamper scalability and reliability, and thus, completely distributed resource information systems, based on Distributed Hash Tables have been proposed. In some cases resource distribution might be highly uneven, load balancing of data becomes thus a crucial problem. However, current load balancing schemes cannot handle large amounts of data corresponding to a single resource type. In this paper we propose therefore RESERV, a distributed information system for Grid applications with a novel load balancing approach, able to handle extreme load unbalance.

The ubiquitous computing paradigm presents researchers with challenges in several fields of information technology. One of these challenges is global connectivity i.e., connectivity regardless of place and time. A reasonable approach to this vision is the best effort extension of the Internet for the users of wireless communication devices. In this paper we present one step toward this goal: a network architecture that provides the multi-hop extension of managed wireless networks, and a routing algorithm which relies on it. The architecture organizes nodes into multiple, dynamic address tree hierarchies based on physical connections. By these means, the architecture enables a multi-path routing mechanism that uses no routing tables, packets being deterministically forwarded based solely on the structure of their destination addresses

Lookup operations in CAN might take a long time, as requests are passed only to direct neighbors. Realities ease this problem, but their random nature does not ensure fairness between participating nodes. In this paper we propose multiple levels of long range neighbors, called Reference Points (RPs), placed in a deterministic manner. Their location is calculated for each node so as to ensure reaching any point in the coordinate space in a comparably small number of steps. Multi-level RPs also bring fairness to CAN.

The expected growth in the number of communicating wireless devices claims for a more effective utilization of widespread wireless technologies, like WiFi. In this paper we propose an addressing and routing network architecture that uses multi-hop access to extend the range of managed wireless networks. It provides a scalable and controllable solution to facilitate network configuration and mobility handling in a possibly heterogeneous environment.

Energy efficiency is a very important goal in wireless sensor networks. In order to achieve it, many recent solutions use mobile sinks that move either randomly or along a predefined path. In this paper we propose an adaptive mobility solution; the sink moves inside the sensor network according to the current events, so as to minimize the energy consumption incurred by the multi-hop transmission of the event-related data.

In this paper we propose new techniques to improve scheduling performance in time-slotted superframe based ad hoc networks. Such channel access technology is used in the IEEE 802.15.3 and 802.15.4 standards. Building on the performance analysis of previous proposals, we enhance the scheduling algorithms with flow state signaling and burst eligibility decision, so as to exploit the features of the 802.15.3 architecture. We show by simulation that the scheduling algorithms extended with these special mechanisms achieve higher performance, with better channel utilization and lower power consumption