The EU-funded EMPYREAN project (empyrean-horizon.eu) aims to establish a hyper-distributed computing paradigm, leveraging collaborative, heterogeneous IoT devices and federated resources. EMPYREAN focuses on developing technologies for efficient AI workload processing, secure distributed edge storage and cloud-native application development. It will offer open and standardised APIs and use open-source platforms. EMPYREAN's capabilities will be demonstrated through three use cases: advanced manufacturing, smart agriculture, and warehouse automation.

The number of IoT devices is expected to continue its dramatic growth in the coming years and, with it, a growth in the amount of data to be transmitted, processed and stored. Compression techniques that support analytics directly on the compressed data could pave the way for systems to scale efficiently to these growing demands. This paper proposes two novel methods for preprocessing a stream of floating point data to improve the compression capabilities of various IoT data compressors. In particular, these techniques are shown to be helpful with recent compressors that allow for random access and analytics while maintaining good compression. Our techniques improve compression with reductions up to 80% when allowing for at most 1% of recovery error.

Distributed storage systems spanning across different cloud data centers have substantially improved availability and flexibility for data storage and retrieval operations. However, stringent latency requirements of emerging applications necessitate optimized selection of storage resources that exhibit smaller delay. Introducing edge resources into distributed storage systems enables data placement closer to its source, but simultaneously increases the complexity of decision-making and orchestration processes for optimal data placement. In this work, we develop mechanisms for storing data across an infrastructure that includes both edge and cloud resources. Our approach focuses on optimizing data integrity, longevity, security, and cost, while leveraging erasure coding when performing the resource allocation. We first present a comprehensive mixed integer linear programming formulation of the storage resource orchestration problem. As the search space for the optimal solution can be vast and the execution time prohibitively large for real size problems, we also propose an innovative multi-agent heuristic approach that uses the rollout, a reinforcement based policy, to balance performance and execution time efficiently. Through various simulation experiments, we evaluate the developed mechanisms and trade-offs involved in our approach. By incorporating data from a multi-cloud provider, we further enhance the validity of the simulations and the conclusions drawn.

Smart electricity meters typically upload power consumption readings once or few times a day. Utility providers aim to increase the upload frequency in order to access consumption information in near real time, but the currently used data compressors fail to provide sufficient savings in this new scenario on the low-bandwidth, high-cost data connection. We propose a new compression method and data format for DLMS smart meter readings, which is significantly better with frequent uploads and makes it feasible to report every reading in near real time with the same or lower data sizes than the currently available compressors in the DLMS protocol.

Utility providers are relying more often on smart, wirelessly connected smart meters to collect consumption information of their customers. The sheer amount of connected smart meters and the growing requirements to provide more frequent reports from each device are putting a large strain on existing systems and protocols. In this paper, we propose three novel lossless compression schemes that significantly reduce the size of standard DLMS data messages uploaded by smart electricity meters. Using real life data sets, we show that these methods can achieve compression rates of over 90% while being transparent to the DLMS protocol.