Related papers: Distributed Path Reconfiguration and Data Forwardi…
Distributed resource allocation (DRA) is fundamental to modern networked systems, spanning applications from economic dispatch in smart grids to CPU scheduling in data centers. Conventional DRA approaches require reliable communication, yet…
Modern enterprise platforms increasingly depend on distributed microservices, analytical data platforms, and external APIs to construct composite responses for applications. Orchestrating data retrieval across these heterogeneous systems is…
Internet of Things (IoT) has emerged as one of the key features of the next generation wireless networks, where timely delivery of status update packets is essential for many real-time IoT applications. To provide users with context-aware…
Summarising distributed data is a central routine for parallel programming, lying at the core of widely used frameworks such as the map/reduce paradigm. In the IoT context it is even more crucial, being a privileged mean to allow long-range…
Distributed Stream Processing (DSP) systems enable processing large streams of continuous data to produce results in near to real time. They are an essential part of many data-intensive applications and analytics platforms. The rate at…
The implementation of optimal power flow (OPF) methods to perform voltage and power flow regulation in electric networks is generally believed to require extensive communication. We consider distribution systems with multiple controllable…
Minimizing energy consumption of low-power wireless nodes is a persistent challenge from the constrained Internet of Things (IoT). In this paper, we start from the observation that constrained IoT devices have largely different hardware…
Information Fusion Systems are now widely used in different fusion contexts, like scientific processing, sensor networks, video and image processing. One of the current trends in this area is to cope with distributed systems. In this…
With the proliferation of Internet of Things (IoT) devices, ensuring secure communications has become imperative. Due to their low cost and embedded nature, many of these devices operate with computational and energy constraints, neglecting…
The Internet of Things (IoT) is the science of connecting multiple devices that coordinate to provide the service in question. IoT environments are complex, dynamic, rapidly changing and resource constrained. Therefore, proactively adapting…
Fog computing offers increased performance and efficiency for Industrial Internet of Things (IIoT) applications through distributed data processing in nearby proximity to sensors. Given resource constraints and their contentious use in IoT…
In this work, we design and analyze novel distributed scheduling algorithms for multi-user MIMO systems. In particular, we consider algorithms which do not require sending channel state information to a central processing unit, nor do they…
A novel approach is presented in this work for context-aware connectivity and processing optimization of Internet of things (IoT) networks. Different from the state-of-the-art approaches, the proposed approach simultaneously selects the…
The innovative services empowered by the Internet of Things (IoT) require a seamless and reliable wireless infrastructure that enables communications within heterogeneous and dynamic low-power and lossy networks (LLNs). The Routing Protocol…
The presence of embedded electronics and communication capabilities as well as sensing and control in smart devices has given rise to the novel concept of cyber-physical networks, in which agents aim at cooperatively solving complex tasks…
Time-critical data aggregation in Internet of Things (IoT) networks demands efficient, collision-free scheduling to minimize latency for applications like smart cities and industrial automation. Traditional heuristic methods, with two-phase…
We consider multi-hop wireless networks serving multiple flows in which only packets that meet hard end-to-end deadline constraints are useful, i.e., if a packet is not delivered to its destination node by its deadline, it is dropped from…
In this paper, dynamic deployment of Convolutional Neural Network (CNN) architecture is proposed utilizing only IoT-level devices. By partitioning and pipelining the CNN, it horizontally distributes the computation load among…
Data intensive applications often involve the analysis of large datasets that require large amounts of compute and storage resources. While dedicated compute and/or storage farms offer good task/data throughput, they suffer low resource…
Increasing need for large-scale data analytics in a number of application domains has led to a dramatic rise in the number of distributed data management systems, both parallel relational databases, and systems that support alternative…