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Robot path planning plays a pivotal role in enabling autonomous systems to navigate safely and efficiently in complex and uncertain environments. Despite extensive research on classical graph-based methods and sampling-based planners,…

Fog computing is a promising computing paradigm for time-sensitive Internet of Things (IoT) applications. It helps to process data close to the users, in order to deliver faster processing outcomes than the Cloud; it also helps to reduce…

Many future Internet of Things (IoT) applications are expected to rely heavily on reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicles (UAVs). However, the endurance of such systems is constrained by the limited onboard…

This paper presents a strategy to allocate services on a Cloud system without overloading nodes and maintaining the system stability with minimum cost. We specify an abstract model of cloud resources utilization, including multiple types of…

With the emergence of more and more applications of Internet-of-Things (IoT) mobile devices (IMDs), a contradiction between mobile energy demand and limited battery capacity becomes increasingly prominent. In addition, in ultra-dense IoT…

Deep-learning-based intelligent services have become prevalent in cyber-physical applications including smart cities and health-care. Collaborative end-edge-cloud computing for deep learning provides a range of performance and efficiency…

Recent increase in energy prices has led researchers to find better ways for capacity provisioning in data centers to reduce energy wastage due to the variation in workload. This paper explores the opportunity for cost saving utilizing the…

Data compression is becoming critical for storing scientific data because many scientific applications need to store large amounts of data and post process this data for scientific discovery. Unlike image and video compression algorithms…

Real-time computation of data streams over affordable virtualized infrastructure resources is an important form of data in motion processing architecture. However, processing such data streams while ensuring strict guarantees on quality of…

Due to the emergence of highly dynamic multimedia applications there is a need for flexible platforms and run-time scheduling support for embedded systems. Dynamic Reconfigurable Hardware (DRHW) is a promising candidate to provide this…

Advanced instruments in a variety of scientific domains are collecting massive amounts of data that must be post-processed and organized to support scientific research activities. Astronomers have been pioneers in the use of databases to…

The optimization of query execution plans is known to be crucial for reducing the query execution time. In particular, query optimization has been studied thoroughly for relational databases over the past decades. Recently, the Resource…

Function-as-a-Service (FaaS) has recently emerged to reduce the deployment cost of running cloud applications compared to Infrastructure-as-a-Service (IaaS). FaaS follows a serverless 'pay-as-you-go' computing model; it comes at a higher…

Researchers in the field of materials science, chemistry, and computational physics are regularly posed with the challenge of managing large and heterogeneous data spaces. The amount of data increases in lockstep with computational…

This paper investigates the optimal signal detection problem with a particular interest in large-scale multiple-input multiple-output (MIMO) systems. The problem is NP-hard and can be solved optimally by searching the shortest path on the…

Scientific workflows are designed as directed acyclic graphs (DAGs) and consist of multiple dependent task definitions. They are executed over a large amount of data, often resulting in thousands of tasks with heterogeneous compute…

In the use of database systems, the design of the storage engine and data model directly affects the performance of the database when performing queries. Therefore, the users of the database need to select the storage engine and design data…

With the increase in agent-based applications, there are now agent systems that support \emph{concurrent} client accesses. The ability to process large volumes of simultaneous requests is critical in many such applications. In such a…

Advances in technology and computing hardware are enabling scientists from all areas of science to produce massive amounts of data using large-scale simulations or observational facilities. In this era of data deluge, effective coordination…

As machine learning (ML) is increasingly implemented in hardware to address real-time challenges in scientific applications, the development of advanced toolchains has significantly reduced the time required to iterate on various designs.…