Network slicing allows Telecom Operators (TOs) to support service provisioning with diverse Service Level Agreements (SLAs). The combination of network slicing and Open Radio Access Network (RAN) enables TOs to provide more customized network services and higher commercial benefits. However, in the current Open RAN community, an open-source end-to-end slicing solution for 5G is still missing. To bridge this gap, we developed ORANSlice, an open-source network slicing-enabled Open RAN system integrated with popular open-source RAN frameworks. ORANSlice features programmable, 3GPP-compliant RAN slicing and scheduling functionalities. It supports RAN slicing control and optimization via xApps on the near-real-time RAN Intelligent Controller (RIC) thanks to an extension of the E2 interface between RIC and RAN, and service models for slicing. We deploy and test ORANSlice on different O-RAN testbeds and demonstrate its capabilities on different use cases, including slice prioritization and minimum radio resource guarantee.

In this paper, we propose a new service orchestration approach for in-network fully distributed dynamic compute composition with limited involvement from the consumer and no centralized coordinator. The service is composed fully inside the network including assembling data and software, and compute node selection, leveraging standardized NDN functionalities. The use of the proposed approach will enable smooth support of dynamic compute services over wireless/mobile edge and edge/fog NDN-based systems, where the use of conventional IP approach faces fundamental difficulties related to dynamic allocation of IP addresses. We will also outline extensions of the proposed approach for streaming data, function chaining and re-use of partially computed results. Our numerical results show that the proposed method improves service reliability at the edge by increasing the Interest satisfaction by 5-10 times depending on the considered deployment, network topology, and resources.

With the rapid growth in number of connected devices, edge and fog computing paradigms become promising solutions for handling computation with low latency and reduced bandwidth usage. They allow to execute a computation across IoT devices, e.g., cameras, an edge aggregator/processor, and on the cloud. In this paper, we consider a pervasive computing scenario where end devices such as laptops, smart phones or roadside units are willing to offer compute services along with the traditional infrastructure-based edge computing resources. Our work uses an Information Centric Network (ICN), which is a promising technique for resource-constrained end devices to offload time-sensitive computing tasks to nearby distributed network resources, e.g., to an edge server. We expand the Named Data Networking (NDN), which is an ICN implementation, to operate in a dynamic resource-constrained multi-hop environment and propose i) a fast and efficient capability discovery scheme to assist each node in discovering other nodes’ capabilities in a distributed manner; and ii) efficient schemes for server/path selection, to optimize the end-to-end latency including discovery, data transmission and computation. Our results show that the proposed solutions can improve the total latency by around 50% for most of the tasks.

This paper describes how user-specific QoS requirements may improve spectral utilization in heterogeneous networks. Throughput improvements of FTP (File Transfer Protocol) applications benefiting from the rate adaptation and MAC scheduling algorithms for video applications that incorporate user-specific QoS to improve system capacity are demonstrated. These improvements are achieved while user satisfaction for video applications, as measured by the Mean Opinion Score (MOS), is maintained at an acceptable level. OPNET system simulations were performed for a set of FTP users and video users that were assigned specific QoS target levels in LTE systems. Simulation results show that significant system capacity improvements and acceptable MOS levels can be achieved for FTP users and video users, respectively, if such user-specific QoS requirements are considered in heterogeneous networks.

This paper presents the proof-of-concept investigation for a miniaturized vectorcardiogram [VCG] system for ambulatory on-body applications that continuously monitors the electrical activity of the heart in three dimensions. We investigate the minimum distance between a pair of leads in the X, Y and Z axes such that the signals are distinguishable from the noise. The target dimensions for our VCG are 3×3×2 cm and, based on our preliminary results, it is possible to achieve these dimensions. The next step in our research is to build the miniaturized VCG system that includes processing, learning and communication capabilities.

Cooperative Networking is a new technology which exploits the massive deployment of nodes in wireless sensor networks. Cooperative Networking synergistically integrates Networking with cluster-based Cooperative Communications to improve reliability and enhance network performance. In this paper, we consider the effect of link-level feedback and retransmissions on the performance of wireless sensor networks using Cooperative Networking, and we present scenarios where link-level retransmission offers a significant improvement in network throughput. Generally, Cooperative Networking with link-level retransmission provides higher throughput when the network node density is low (i.e., sparse networks) or in environments with adverse conditions such as high probability of transmission loss and low connectivity among the nodes.