In various IoT applications it is essential that the autonomous connected devices have the most up-to-date information, either to be read or posted. In this work, we consider a general setting of a wireless multihop network with multiple data flows and study the interactions of backpressure routing, congestion control, clean energy harvesting and Age-of-Information (AoI). We propose a heuristic scheme that is based on optimal source data rates and routing decisions, enhanced with heuristically determined AoI-based features in the form of a weight scaling method. The feasibility of the solution in terms of satisfying queue stability is proven, and the obtained upper bound on the queue lengths depends on the quotient of the max versus the min weight value. Numerical evaluations point out the improvements of the heuristic scheme in terms of delivering fresh information with priority, as well as the tradeoffs between AoI and optimality.

Green operation is of paramount importance in 6G and zero-energy wireless nodes can support it. To fully exploit the potentials of zero-energy wireless multi-hop networks, it is essential to jointly optimize their source data rates, routing and transmission power decisions, which is a significantly complex problem, in particular under the uncertainties introduced by the wireless channel states and the energy harvesting processes on the nodes. In this paper, we tackle the aforementioned problem under the assumption that wireless nodes operate only based on their batteries that charge solely via ambient energy harvesting. A plan for the network operation for a future time horizon is computed using scenario-based optimization techniques to account for stochasticities. The derived problem formulation is non-convex and is solved via a novel heuristic method that iteratively solves appropriately parameterized convex approximations of the original problem. At convergence, the obtained solution is feasible to the original non-convex problem. Numerical results illustrate the effectiveness of the proposed solution compared to the standard non-convex solver Ipopt and showcase the behavior of the network under heterogeneous scenarios.

In time-sensitive networks, bounds on worst-case delays are typically obtained by using network calculus and assuming that flows are constrained by bit-level arrival curves. However, in IEEE TSN or IETF DetNet, source flows are constrained on the number of packets rather than bits. A common approach to obtain a delay bound is to derive a bit-level arrival curve from a packet-level arrival curve. However, such a method is not tight: we show that better bounds can be obtained by directly exploiting the arrival curves expressed at the packet level. Our analysis method also obtains better bounds when flows are constrained with g-regulation, such as the recently proposed Length-Rate Quotient rule. It can also be used to generalize some recently proposed network-calculus delay-bounds for a service curve element with known transmission rate.

Twitter constitutes an accessible platform for studying and experimenting with the dynamics of information dissemination. By exploiting this and using real data, in this paper, we study the temporal dynamics of topic-specific information spread in Twitter, where we assume that each topic corresponds to a hashtag. We develop an epidemic model for information spread in Twitter and we validate it using real data for several hashtags chosen so as to cover a variety of characteristics. Contrary to the existing works in literature, which define the informed Twitter users as those who have produced/reproduced tweets with a specific hashtag, our model considers as informed a superset of Twitter users who have seen/produced/reproduced tweets with a specific hashtag. Thus, it does not underestimate the extent of information propagation in the network. The evaluation results indicate a satisfactory performance of the proposed epidemic model for all hashtag types examined; while more importantly, they allow studying the impact of several factors, such as the need of time-varying infection rates depending on the hashtag type.

In this paper we introduce and demonstrate new recommendation algorithms for large-scale online systems, such as e-shops and cloud services. The proposed algorithms are based on the combination of network embedding in hyperbolic space with greedy routing, exploiting properties of hyperbolic metric spaces. Contrary to the existing recommender systems that rank products in order to propose the highest ranked ones to the users, our proposed recommender system creates a progressive path of recommendations towards a final (known or inferred) target product using greedy routing over networks embedded in hyperbolic space. Thus, it prepares the user by intermediate recommendations for maximizing the chances that he/she accepts the recommendation of the target product(s). This casts the problem of locating a suitable recommendation as a path problem, where leveraging on the efficiency of greedy routing in graphs embedded in hyperbolic spaces and exploiting special network structure, if any, pays dividends. Two variants of our recommendation approach are provided, namely Hyperbolic Recommendation-Known Destination (HRKD), Hyperbolic Recommendation-Unknown Destination (HRUD), when the target product is known or unknown, respectively. We demonstrate how the proposed approach can be used for producing efficient recommendations in online systems, along with studying the impact of the several parameters involved in its performance via proper emulation of user activity over suitably defined graphs.

In this paper, we formulate a network utility maximization (NUM) problem, targeting an optimal cross-layer network operation, while considering time-varying and random possibly non-stationary wireless channels. As indicated in the literature, this problem imposes scalability constraints when the time horizon of the network control increases, impeding an online (i.e., real-time) application of its solution during the network operation. To achieve an online network control, in this paper, we leverage on model predictive control (MPC) or receding horizon control (RHC) for the solution of the NUM problem. Furthermore, MPC/RHC allows for the adaptation of the optimal controls in dynamic and evolving network conditions, in our case with respect to the wireless channels, the modeling parameters of which are estimated in an online fashion. We present and analyze the NUM problem, while we appropriately reformulate it for applying MPC/RHC. Then, we describe the MPC/RHC-based algorithmic solution, which determines the decisions for the online network control including power control, scheduling, routing, and congestion control, while we discuss stability and optimality issues. Finally, we evaluate the proposed methodology via numerical results and we show that the performance lies very close to the optimal one even for relatively small receding horizon lengths that significantly reduce the computational time complexity.

In this paper, we study, analyze, and evaluate a performance-aware cross-layer design approach for wireless multihop networks. Through network utility maximization (NUM) and weighted network graph modeling, a cross-layer algorithm for performing jointly routing, scheduling, and congestion control is introduced. The performance awareness is achieved by both the appropriate definition of the link weights for the corresponding application's requirements and the introduction of a weighted backpressure (BP) routing/scheduling. Contrary to the conventional BP, the proposed algorithm scales the congestion gradients with the appropriately defined per-pair (link, destination) weights. We analytically prove the queue stability achieved by the proposed cross-layer scheme, while its convergence to a close neighborhood of the optimal source rates' values is proven via an ε-subgradient approach. The issue of the weights' assignment based on various quality-of-service (QoS) metrics is also investigated. Through modeling and simulation, we demonstrate the performance improvements that can be achieved by the proposed approach-when compared against existing methodologies in the literature-for two different examples with diverse application requirements, emphasizing respectively on delay and trustworthiness.

Network Utility Maximization (NUM) is often applied for the cross-layer design and optimization of wireless networks. In most approaches, the NUM framework is based on the assumption of known or ideal wireless channel conditions. However, realistic wireless channel capacities are stochastic (time-varying and random) bearing time-varying statistics, necessitating the redesign and solution of NUM problems to capture such effects. In this paper, we apply the NUM framework to perform congestion and power control in wireless multihop networks while taking into account the stochastic Long Term Fading (LTF) wireless channels. Specifically, the wireless channel power loss is modeled via the use of Stochastic Differential Equations (SDEs) alleviating several assumptions that exist in state of the art channel modeling within the NUM framework such as the finite number of channel states or the stationarity. Based on that, we initially propose an algorithm for performing congestion control under stochastic LTF wireless channels. Next, the proposed algorithm is enhanced via power control aiming to further increase users' optimal utility by exploiting the random reductions of the stochastic channel power loss while also considering energy efficiency. Finally, numerical results are presented to evaluate the performance and operation of the proposed approach.

Network utility maximization is often applied for the cross-layer design of wireless networks considering known wireless channels. However, realistic wireless channel capacities are stochastic bearing time-varying statistics, necessitating the redesign and solution of NUM problems to capture such effects. Based on NUM theory we develop a framework for scheduling, routing, congestion control and power control in wireless multihop networks that considers stochastic long or short term fading wireless channels. Specifically, the wireless channel is modeled via stochastic differential equations alleviating several assumptions that exist in state-of-the-art channel modeling within the NUM framework such as the finite number of states or the stationarity. Our consideration of wireless channel modeling leads to a NUM problem formulation that accommodates non-convex and time-varying utilities. We consider both cases of non orthogonal and orthogonal access of users to the medium. In the first case, scheduling is performed via power control, while the latter separates scheduling and power control and the role of power control is to further increase users' optimal utility by exploiting random reductions of the stochastic channel power loss while also considering energy efficiency. Finally, numerical results evaluate the performance and operation of the proposed approach and study the impact of several involved parameters on convergence.

Achieving high Quality of Service (QoS) over wireless multihop networks calls for enhanced routing/scheduling algorithms. Towards this direction it has been shown in the literature that the Greedy Backpressure algorithm which combines routing based on greedy hyperbolic embedding with backpressure scheduling, achieves to improve delay while remains throughput optimal. However, the performance of such an approach is significantly affected by the selection of the corresponding spanning tree used for greedily embedding the network into the hyperbolic space. Our work aims exactly at addressing this issue, that is the construction of an appropriate spanning tree that improves the cost of the paths used by the Greedy Backpressure approach, when considering a more generic weighted network graph modeling. The latter allows us to take into consideration the link costs in the routing process, which in turn may result in the simultaneous improvement of multiple performance metrics. To address the problem under consideration, we propose a coalition formation game framework among the network nodes, so that they can decide cooperatively for the spanning tree, via trading their value functions designed to depend on the link weights. We prove that the stable outcome of the coalitional game is a spanning tree of the network, and study through simulations the induced improvement in the network performance. Furthermore, we extend the framework for a scenario with multiple costs on each link through multi-tree hyperbolic embedding.

Future communications consist of an increasing number of wireless parts, while simultaneously need to support the widespread multimedia applications imposed by social networks. These human-machine systems, driven by both real time social interactions and the challenges of the wireless networks' design, call for efficient and easy to implement, distributed cross-layer algorithms for their operation. Performance metrics such as throughput, delay, trust, energy consumption, need to be improved and optimized aiming at high quality communications. We investigate the coveted throughput-delay trade-off in static wireless multihop networks based on a "computer-aided" design of the backpressure scheduling/routing algorithm for networks embedded in hyperbolic space. Both routing and scheduling exploit the hyperbolic distances to orient the packets to the destination and prioritize the transmissions correspondingly. The proposed design provides us with the freedom of controlling its theoretical throughput optimality and of counterbalancing its practical performance through simulations, leading to significant improvements of the throughput-delay trade-off.

Contemporary traffic demands call for efficient infrastructures capable of sustaining increasing volumes of social communications. In this work, we focus on improving the properties of wireless multihop networks with social features through network evolution. Specifically, we introduce a framework, based on inverse Topology Control (iTC), for distributively modifying the transmission radius of selected nodes, according to social paradigms. Distributed iTC mechanisms are proposed for exploiting evolutionary network churn in the form of edge/node modifications, without significantly impacting available resources. We employ continuum theory for analytically describing the proposed top-down approach of infusing social features in physical topologies. Through simulations, we demonstrate how these mechanisms achieve their goal of reducing the average path length, so as to make a wireless multihop network scale like a social one, while retaining its original multihop character. We study the impact of the proposed topology modifications on the operation and performance of the network with respect to the average throughput, delay, and energy consumption of the induced network.

In this paper the problem of enhancing trust establishment in multi-hop wireless networks is addressed. Exploiting small-world features and based on preferential attachment and initial node trust values, we design inverse Topology Control methods that achieve to reduce the mean hop-distance between two nodes and increase the average trust value of the shortest paths. Based on continuum theory a mathematical framework is developed for the overall socially-motivated trust-based network churn mechanism. Analytical and simulation results exhibit the effectiveness of the proposed approaches for enhancing physical topologies, increasing the average trust path values, and thus further securing future communications systems.