Cloud and edge computing have proposed different functionalities to enable multiple applications requiring different communication, computing, and caching (3C) resources. The upcoming futuristic applications (e.g., metaverse, holographic, and haptic communication) impose further stringent requirements (e.g., ultra-low latency, ultra-high reliability) on the infrastructure. These requirements call for a paradigm shift in the infrastructure architecture where all resource components and owners collaborate from the cloud up to the edge, creating a cloud-to-edge continuum of integrated resources. Furthermore, we argue that artificial intelligence (AI) and collaborative-based decisions are promising techniques to efficiently manage the highly complex architecture that jointly leverages 3C in the continuum. This article presents a comprehensive survey of existing research, including AI and collaborative-based studies, targeting the effective and seamless provision of 3C resources and services in the cloud-to-edge continuum. Through an extensive analysis of driving use cases, the synergy between these three main services is scrutinized to highlight its crucial role in the next-generation network infrastructures (NGNI). Finally, a discussion on the opportunities and challenges brought by integrating 3C in NGNI from different perspectives, including architectural design as well as the regulatory and business aspects, are presented.

The unprecedented increase of heterogeneous devices connected to the Internet, along with tight requirements of future networks, including 5G and beyond, poses new design challenges to network infrastructures. Collaborative computing, caching and communication paradigm together with artificial intelligence have the potential to enable the Next-Generation Networking Infrastructure (NGNI) that is needed to fulfill the stringent requirements of emerging applications. In this paper, we propose the SCORING project vision for reshaping the current network infrastructure towards an NGNI acting as a truly distributed, collaborative, and pervasive system that enables the execution of application-specific tasks and the storage of the related data contents in the Cloud-Edge-Mist continuum with high QoS/QoE guarantees.

In multiuser multiple-input-multiple-output (MIMO) networks, receivers decode multiple concurrent signals using successive interference cancellation (SIC). With SIC, a weak target signal can be deciphered in the presence of stronger interfering signals. However, this is only feasible if each strong interfering signal satisfies a signal-to-noise-plus-interference ratio (SINR) requirement. This necessitates the appropriate selection of a subset of links that can be concurrently active in each receiver's neighborhood; in other words, a subtopology consisting of links that can be simultaneously active in the network is to be formed. If the selected subtopologies are of small size, the delay between the transmission opportunities on a link increases. Thus, care should be taken to form a limited number of subtopologies. We find that the problem of constructing the minimum number of subtopologies such that SIC decoding is successful with a desired probability threshold is NP-hard. Given this, we propose MUSIC, a framework that greedily forms and activates subtopologies in a way that favors successful SIC decoding with a high probability. MUSIC also ensures that the number of selected subtopologies is kept small. We provide both a centralized and a distributed version of our framework. We prove that our centralized version approximates the optimal solution for the considered problem. We also perform extensive simulations to demonstrate that: 1) MUSIC forms a small number of subtopologies that enable efficient SIC operations; the number of subtopologies formed is at most 17% larger than the optimum number of topologies, discovered through exhaustive search (in small networks); 2) MUSIC outperforms approaches that simply consider the number of antennas as a measure for determining the links that can be simultaneously active. Specifically, MUSIC provides throughput improvements of up to four times, as compared to such an approach, in various topological settings. The improvements can be directly attributable to a significantly higher probability of correct SIC based decoding with MUSIC.

In this paper, we shed light on the cross-layer interactions between the PHY, link and routing layers in networks with MIMO links operating in the diversity mode. Many previous studies assume an overly simplistic PHY layer model that does not sufficiently capture these interactions. We show that the use of simplistic models can in fact lead to misleading conclusions with regards to the higher layer performance with MIMO diversity. Towards understanding the impact of various PHY layer features on MIMO diversity, we begin with a simple but widely-used model and progressively incorporate these features to create new models. We examine the goodness of these models by comparing the simulated performance results with each, with measurements on an indoor 802.11 n testbed. Our work reveals several interesting cross-layer dependencies that affect the gains due to MIMO diversity. In particular, we observe that relative to SISO links: (a) PHY layer gains due to MIMO diversity do not always carry over to the higher layers, (b) the use of other PHY layer features such as FEC codes significantly influence the gains due to MIMO diversity, and (c) the choice of the routing metric can impact the gains possible with MIMO.

In Multi-User MIMO networks, receivers decode multiple concurrent signals using Successive Interference Cancellation (SIC). With SIC a weak target signal can be deciphered in the presence of stronger interfering signals. However, this is only feasible if each strong interfering signal satisfies a signal-to-noise-plus-interference ratio (SINR) requirement. This necessitates the appropriate selection of a subset of links that can be concurrently active in each receiver's neighborhood; in other words, a sub-topology consisting of links that can be simultaneously active in the network is to be formed. If the selected sub-topologies are of small size, the delay between the transmission opportunities on a link increases. Thus, care should be taken to form a limited number of sub-topologies. We find that the problem of constructing the minimum number of sub-topologies such that SIC decoding is successful with a desired probability threshold, is NP-hard. Given this, we propose MUSIC, a framework that greedily forms and activates sub-topologies, in a way that favors successful SIC decoding with a high probability. MUSIC also ensures that the number of selected sub-topologies is kept small. We provide both a centralized and a distributed version of our framework. We prove that our centralized version approximates the optimal solution for the considered problem. We also perform extensive simulations to demonstrate that (i) MUSIC forms a small number of sub-topologies that enable efficient SIC operations; the number of sub-topologies formed is at most 17% larger than the optimum number of topologies, discovered through exhaustive search (in small networks). (ii) MUSIC outperforms approaches that simply consider the number of antennas as a measure for determining the links that can be simultaneously active. Specifically, MUSIC provides throughput improvements of up to 4 times, as compared to such an approach, in various topological settings. The improvements can be directly attributable to a significantly higher probability of correct SIC based decoding with MUSIC.

With fully directional communications, nodes must track the positions of their neighbors so that communication with these neighbors is feasible when needed. Tracking process introduces an overhead, which increases with the number of discovered neighbors. The overhead can be reduced if nodes maintain only a subset of their neighbors; however, this may increase the length of paths between node pairs in the network. In this work, we study the tradeoffs between node degree and path stretch. We first design a topology control algorithm to optimize this tradeoff. Assuming that nodes communicate with their directional neighbors using circular directional transmissions, we model the original graph as a unit disk graph (UDG). Given a UDG G, our algorithm finds a sparse subgraph G' with a maximum degree of 6, and connecting each node pair u,v by a path of length hopsG(u, v) = O(hopsG(u, v) + log Delta), where Delta is the maximum degree in G, hopsG'(u, v) denotes length of the shortest path between u, v in G. We show that this result is near-optimal. Based on the insights gained from this design, we next construct a simpler, more practical scheme that integrates fully-directional neighbor discovery and maintenance with topology control strategy. We simulate both algorithms and compare their performances.

With directional antennas, it is extremely important that a node maintains information with regards to the positions of its neighbors. This would allow the node to "track" the neighbors as they move; otherwise, a node will have to resort to either omnidirectional or circular directional transmissions (or receptions) fairly often. This can be overhead intense and can reduce spatial reuse. Maintaining directional information with regards to a large number of neighbors can itself be expensive; therefore it is important to limit a node's degree. We propose a topology control scheme (Di-ATC) that works with fully directional communications and offers a low degree bound while preserving network connectivity. The key idea is to execute Di-ATC on the discovered neighbors to select and maintain connectivity with only a subset of these neighbors. The members of this subset are those with high angular separations. We perform extensive simulations and demonstrate that our scheme effectively limits node degree while at the same time, preserves network connectivity and achieves low path stretch

Our objective in this paper is to design topology control algorithms such that (i) nodes have low degree and (ii) paths in the network have few hops. Low node degree is desirable in networks equipped with smart antennas and to reduce access contention. Short paths are desirable for minimizing communication delays and for better robustness to channel impairments and to mobility. Given any arbitrary unit-disc graph G representing all feasible links, our algorithms find a sparse subgraph G' having a maximum node degree of six and, for each pair of vertices u, v, having hopsG'(u, v) = O(hopsG(u,v) + logDelta), where Delta is the maximum node degree in G and hopsG(u, v) denotes the shortest path length from u to v in G. This result is near-optimal: (i) there is a connected UDG G in which no connected subgraph has degree less than five, and (ii) for any graph G, any bounded-degree subgraph G' must have hopsG'(u, v) = Omega(hopsG(u, v) + logDelta) for some u, v. Our distributed algorithm scales, preserves link symmetry, does not need node synchronization, and requires only O(n) messages. We perform extensive simulations that quantify the performance of our algorithm in realistic scenarios