In the aftermath of natural disasters, Human Assistance & Disaster Recovery (HADR) operations have to deal with disrupted communication networks and constrained resources. Such harsh conditions make high-communication-overhead ML approaches — either centralized or distributed — impractical, thus hindering the adoption of AI solutions to implement a critical function for HADR operations: building accurate and up-to-date situational awareness. To address this issue we developed Roaming Machine Learning (RoamML), a novel Distributed Continual Learning Framework designed for HADR operations and based on the premise that moving an ML model is more efficient and robust than either large dataset transfers or frequent model parameter updates. RoamML deploys a mobile AI agent that incrementally train models across network nodes containing yet unprocessed data; at each stop, the agent initiate a local training phase to update its internal ML model parameters. To prioritize the processing of strategically valuable data, RoamML Agents follow a navigation system based upon the concept of Data Gravity, leveraging Multi-Criteria Decision Making techniques to simultaneously consider many objectives for Agent routing optimization, including model learning efficiency and network resource utilization, while seamlessly blending subjective insights from expert judgments with objective metrics derived from quantifiable data to determine each next hop. We conducted extensive experiments to evaluate RoamML, demonstrating the framework’s efficiency to train ML models under highly dynamic, resource-constrained environments. RoamML achieves similar performance to centralized ML training under ideal network conditions and outperforms it in a more realistic scenario with reduced network resources, ultimately saving up to 75% in bandwidth utilization across all experiments. • Human Assistance & Disaster Recovery (HADR) requires accurate situational awareness. • Most distributed ML approaches assume a stable network and are unsuited for HADR. • Approaches based on distributed AI agents and continual learning are more resilient. • Data Gravity represents a solid foundational concept for agent routing optimization. • Data Gravity and MCDM allow to prioritize the processing of critical datasets.

Machine learning offers a promising avenue for improving the efficiency and effectiveness of decision-making in disaster recovery and relief efforts. These operations face significant hurdles due to the large volumes of data, intermittent connectivity, and infrastructure limitations. In this paper, we present the RoamML Platform, a sophisticated modular implementation of the RoamML framework, designed specifically to address these challenges and enable efficient distributed machine learning. We advocate for a foundational principle that "the transmission of the ML model itself is usually more efficient than the costly transfer of large datasets", leading to a more adaptable training regime. The platform orchestrates the activities of the RoamML model along with its related metadata, collectively referred to as the "RoamML Agent", while faithfully observing the Data Gravity principle to guarantee thorough model training. We extensively validated the platform through a simulated disaster recovery scenario employing the Mininet-WiFi emulator. Our results highlight the benefits of integrating the RoamML framework, including enhanced ML performance and significant bandwidth savings.

Recent Internet-of-Things (IoT) networks span across a multitude of stationary and robotic devices, namely unmanned ground vehicles, surface vessels, and aerial drones, to carry out mission-critical services such as search and rescue operations, wildfire monitoring, and flood/hurricane impact assessment. Achieving communication synchrony, reliability, and minimal communication jitter among these devices is a key challenge both at the simulation and system levels of implementation due to the underpinning differences between a physics-based robot operating system (ROS) simulator that is time-based and a network-based wireless simulator that is event-based, in addition to the complex dynamics of mobile and heterogeneous IoT devices deployed in a real environment. Nevertheless, synchronization between physics (robotics) and network simulators is one of the most difficult issues to address in simulating a heterogeneous multi-robot system before transitioning it into practice. The existing TCP/IP communication protocol-based synchronizing middleware mostly relied on Robot Operating System 1 (ROS1), which expends a significant portion of communication bandwidth and time due to its master-based architecture. To address these issues, we design a novel synchronizing middleware between robotics and traditional wireless network simulators, relying on the newly released real-time ROS2 architecture with a masterless packet discovery mechanism. Additionally, we propose a ground and aerial agents' velocity-aware customized QoS policy for Data Distribution Service (DDS) to minimize the packet loss and transmission latency between a diverse set of robotic agents, and we offer the theoretical guarantee of our proposed QoS policy. We performed extensive network performance evaluations both at the simulation and system levels in terms of packet loss probability and average latency with line-of-sight (LOS) and non-line-of-sight (NLOS) and TCP/UDP communication protocols over our proposed ROS2-based synchronization middleware. Moreover, for a comparative study, we presented a detailed ablation study replacing NS-3 with a real-time wireless network simulator, EMANE, and masterless ROS2 with master-based ROS1. Our proposed middleware attests to the promise of building a large-scale IoT infrastructure with a diverse set of stationary and robotic devices that achieve low-latency communications (12% and 11% reduction in simulation and reality, respectively) while satisfying the reliability (10% and 15% packet loss reduction in simulation and reality, respectively) and high-fidelity requirements of mission-critical applications.

Fast and efficient transport protocols are the foundation of an increasingly distributed world. The burden of continuously delivering improved communication performance to support next-generation applications and services, combined with the increasing heterogeneity of systems and network technologies, has promoted the design of Congestion Control (CC) algorithms that perform well under specific environments. The challenge of designing a generic CC algorithm that can adapt to a broad range of scenarios is still an open research question. To tackle this challenge, we propose to apply a novel Reinforcement Learning (RL) approach. Our solution, MARLIN, uses the Soft Actor-Critic algorithm to maximize both entropy and return and models the learning process as an infinite-horizon task. We trained MARLIN on a real network with varying background traffic patterns to overcome the sim-to-real mismatch that researchers have encountered when applying RL to CC. We evaluated our solution on the task of file transfer and compared it to TCP Cubic. While further research is required, results have shown that MARLIN can achieve comparable results to TCP with little hyperparameter tuning, in a task significantly different from its training setting. Therefore, we believe that our work represents a promising first step towards building CC algorithms based on the maximum entropy RL framework.

Advances in Internet-of-Things, artificial intelligence, and ubiquitous computing technologies have contributed to building the next generation of context-aware heterogeneous systems with robust interoperability to control and monitor the environmental variables of smart environments. Motivated by this, we propose HeteroSys, an end-to-end multi-functional smart IoT-based system prototype for heterogeneous and collaborative sensing in a smart IoT-based environment. A unique characteristic of HeteroSys is that it relies on Home Assistant (HA) to collate heterogeneous sensors (e.g., passive infrared sensors (PIR), reed (door) switches, object tags, wearable wrist-mounted, water leak sensors, and internet protocol cameras), and uses a variety of networking protocols such as Zigbee open standard for mesh networking, WiFi, and Bluetooth Low Energy (BLE) for communication. The reliance on HA (and its broad community support) makes HeteroSys ideal for various applications such as object detection, human activity recognition and behavior patterns. We articulated the development phase, integration, testing challenges and evaluation of the HeteroSys. We conducted an extensive 24-hour longitudinal data collection from 5 participants performing 6 activities by deploying in an indoor home environment. Our assessment of the acquired dataset reveals that the representations learned using deep learning architecture aid in improving the detection of activities to 83.1% accuracy.

Smart Cities are among the most dynamic and rapidly evolving modern environments, driven by the development of new technologies and the fast growth of the Internet of Things (IoT), which enable the acquisition and processing of very large amounts of data.However, accessing IoT assets is proving to be a challenge, as neither formal nor de facto standards to discover connected Things have emerged.Services that provide discovery and access capability for IoT resources are in the rise, but they often adopt service-specific interfaces and authorization mechanisms that hinder the development and maintainability of IoT applications.Low flexibility and interoperability become especially problematic during emergency situations, when responders might need to access resources that normally would not be allowed to access.To address these issues, this paper describes MARGOT, a distributed edge computing platform that supports domain-aware and secure discovery of IoT resources in Smart Cities.Experimental results obtained using MARGOT in an emulated network environment show that our platform can effectively reduce discovery latency and bandwidth consumption under the considered use cases and network conditions.

Service fabric management in Fog Computing is a challenging task, which has to deal with a complex and resource scarce environment. We argue that approaches leveraging Value-of-Information (VoI) concepts and tools are particularly interesting to support the realization of that objective. This paper describes innovative methodologies and reference models for the service fabric management for Fog Computing applications. First, we formalize the VoI concept and discuss its adoption in Fog Computing environments. Then, we propose a formal model that aims at maximizing the allocation of Fog services from a value-based perspective. To overcome the complexity of this model, we present two possible approaches (simulation-based optimization and a model approximation) and we compare them by adopting Evolutionary Algorithms (EAs) as optimization techniques. Experimental results prove the validity of both models in finding resource allocation solutions capable of minimizing network latency and maximizing the utility for the end-users of Fog Computing services. Finally, we show how the results of the approximated model can be adopted as a first approximated approach for resource management of Fog Computing services.

The IEEE International Conference on Self-Adapting and Self-Organizing Systems (SASO) is the main forum for studying and discussing the foundations of a principled approach to engineering systems, networks, and services based on self-adaptation and self-organization. Over the past decade, it has consolidated as the primary scientific conference for sharing ideas on algorithms, technologies, tools, and applications across a wide range of scientific fields. In 2016, the conference was hosted by the University of Augsburg, in Augsburg, Germany; its scientific program comprised full papers, short papers, poster and demo presentations, workshops, doctoral symposium and tutorials. This special issue of ACM TAAS champions some of the most solid research results of SASO 2016, presenting selected, revised, and extended best articles.

Managing the extremely large volume of information generated by Internet-of-Things (IoT) devices, estimated to be in excess of 400 ZB per year by 2018, is going to be an increasingly relevant issue. Most of the approaches to IoT information management proposed so far, based on the collection of IoT-generated raw data for storage and processing in the Cloud, place a significant burden on both communications and computational resources, and introduce significant latency. IoT applications would instead benefit from new paradigms to enable definition and deployment of dynamic IoT services and facilitate their use of computational resources at the edge of the network for data analysis purposes, and from smart dissemination solutions to deliver the processed information to consumers. This paper presents SPF (as in “Sieve, Process, and Forward”), an SDN solution which extends the reference ONF architecture replacing the Data Plane with an Information Processing and Dissemination Plane. By leveraging programmable information processors deployed at the Internet/IoT edge and disruption tolerant information dissemination solutions, SPF allows to define and manage IoT applications and services and represents a promising architecture for future urban computing applications.

In the near term, a multitude of IoT applications are expected, each taking advantage of heterogeneous device collections ranging from environmental sensors to smartphones. However, approaches taken in many IoT systems - based on the paradigm of Cloud computing - face challenges of both high latency and network utilization. A clear demand now exists for new paradigms to facilitate IoT application usage of computational resources at the edge of the network for data analysis purposes, as well as smart dissemination solutions to deliver information to consumers. This paper presents SPF (Sieve, Process, and Forward), a Software Defined Networking (SDN) solution for creating and managing IoT applications and services. By leveraging programmable information processors deployed at the Internet/IoT edge, the SDN approach introduced by SPF represents a promising architecture for future urban computing applications.

Service-oriented architectures (SoAs) are a popular paradigm for enterprise and data center computing but normally do not perform well on tactical networks, which are often degraded in terms of bandwidth, reliability, latency, and connectivity. This paper presents the agile computing middleware and in particular a transparent network proxy and associated protocols that help address the impedance mismatch that occurs between SoAs and tactical and DIL (Disconnected, Intermittent, and Limited) networks.

Tactical edge networks provide one of the most challenging environments for communications, which significantly complicates the development of efficient and robust information dissemination solutions. In our previous work, we found that exploiting highly mobile nodes, such as Unmanned Air Vehicles, with cyclic mobility patterns, as message ferries can significantly improve the performance of information dissemination solutions. However, our experience demonstrated that robust forecasting mechanisms are essential in order to withstand frequent changes in the mobility patterns of message ferrying nodes. This paper presents an extension of the adaptive node presence forecasting component developed for DisService, a Peer-to-peer information dissemination system, that provides estimates of tolerance and accuracy of node mobility forecasts. We tested the extended forecasting mechanism in a simulation environment and found that it can lead to significant improvements in the timeliness and reliability of information dissemination.

Rising manned air traffic and deployment of unmanned aerial vehicles in complex operations requires integration of innovative and autonomous conflict detection and resolution methods. In this paper, the task of conflict detection and resolution is defined as an optimization problem searching for a heading control for cooperating airplanes using communication. For the optimization task, an objective function integrates both collision penalties and efficiency criteria considering airplanes' objectives (waypoints). The probability collectives optimizer is used as a solver for the specified optimization task. This paper provides two different implementation approaches to the presented optimization-based collision avoidance: 1) a parallel computation using multiagent deployment among participating airplanes and 2) semicentralized computation using the process-integrated-mechanism architecture. Both implementations of the proposed algorithm were implemented and evaluated in a multiagent airspace test bed AGENTFLY. The quality of the solution is compared with a negotiation-based cooperative collision avoidance method - an iterative peer-to-peer algorithm.

Taking advantage of the benefits of modern networking, a growing number of users are exhibiting mobile behavior. As they roam between different network localities, they access the Internet and the Web exploiting both wired and wireless communications and using several heterogeneous devices. Mobile users want to access their subscribed services anywhere, anytime, and want to preserve their currently opened service sessions as they roam between different network localities or switch between different devices. Mobile userspsila requirements call for novel middlewares to provide support for mobility on top of the traditional Internet infrastructure. In this context, we have developed Mockets, a communication middleware specifically designed to address the challenges of wireless networks and mobile computing. In particular, Mockets supports session mobility in terms of seamless handover for preservation of end-to-end connectivity in spite of node mobility, automatic detection and exploitation of best available connectivity, and migration of service session endpoints from one node to another.

Complex services in service-oriented architectures such as the grid typically require to be configured in multiple ways that cannot be anticipated by service designers; we illustrate this requirement by studying the myGrid registry, a grid registry capable of supporting annotations of service descriptions by third-party users. Instead, services have to be conceived so that they can be configured at deployment and run time. We argue that KAoS is a powerful and flexible language that can help define such configurations. Using our registry case study, we examine the requirements that the definition of such complex configurations brings on policy languages and explain how they can be satisfied. Specifically, we use role-value maps to express constraints between property values; we introduce a notion of PolicySet with associated parameters that support constraints within a well defined scope; finally, we define a notion of context that allows us to refer to property values that were extant in past execution environments. Essentially, these concepts allow us to add constraints to values in policy definitions, to organise policies in coherent and structure blocks, and to refer to the execution history. The paper discusses these concepts and how they are implemented in a binding of the KAoS policy language to the myGrid registry.

Agile computing may be defined as opportunistically (or on user demand) discovering and taking advantage of available resources in order to improve capability, performance, efficiency, fault tolerance, and survivability. The term agile is used to highlight both the need to quickly react to changes in the environment as well as the need to exploit transient resources only available for short periods of time. Agile computing builds on current research in grid computing, ad-hoc networking, and peer-to-peer resource sharing. This paper describes both the general notion of agile computing as well as one particular approach that exploits mobility of code, data, and computation. Some performance metrics are also suggested to measure the effectiveness of any approach to agile computing.

While You're Away (WYA) is a distributed system that aggregates the computational power of individual computer systems. WYA introduces the notion of roaming computations-Java-based programs that move around the network utilizing the resources of idle workstations. WYA provides architectural independence and addresses issues of convenience, security and incentive for owners of workstations. WYA is based on the NOMADS mobile agent system, which uses the Aroma Virtual Machine (VM) to provide strong mobility, resource control, and resource accounting. WYA currently runs on Win32 and UNIX workstations but is being extended to work on other computational devices such as television set-top boxes, video game consoles, and Internet appliances.