Non-terrestrial networks (NTNs) contribute significantly to offering connectivity services for the upcoming sixth generation (6G). However, many services demand real-time uplink data transmission. Owing to the dynamic topology, instability of NTN channel status, resource constraints, and tremendous mobile users on ground networks; resilient and effective resource management and channel accessibility pose substantial challenges. This work investigates the uplink sum-rate maximization and latency minimization problems with a channel allocation scheme for user equipments (UEs) to high-altitude platforms (HAPs) of a particular NTN when UEs simultaneously transmit data to HAPs and HAPs forward it to low-earth orbit satellite (LEO-sat) via multiple subcarriers. We formulate a fairness resource allocation optimization problem reflecting the system utility and load of the HAP-LEO-sat link for aerial-to-space uplinks. To tackle the high-dimensional non-convex optimization problems and short visibility of LEO-sat in the learning, we advocate a novel deep reward-based multi-agent reinforcement learning (DeepRMARL) with prioritized experience replay (PER) and priority sampling approach (PSA). Furthermore, we present a long-short-term memory (LSTM) model for future environment channel prediction due to time-varying channels between UEs and HAPs. The results explicitly unveil the feasibility of our method in optimizing total uplink rate, achieving a higher fairness index, minimizing latency by approx. 12.2%, and predicting future environment states with approx. 92.8% accuracy.

Non-terrestrial networks (NTNs) are crucial for 6G communications but face challenges like classical communication bottlenecks. Semantic communication (SemCom) offers a solution, but efficient resource allocation remains unresolved. This paper proposes a joint approach for resource allocation and semantic information selection in low-earth orbit (LEO) satellite-based image transmission. A novel metric, Quality of Semantic (QoSem), is introduced to measure the SemCom performance. An optimization problem is formulated to maximize QoSem by allocating computing, power, and bandwidth resources and selecting optimal semantic information. Furthermore, we propose a novel deep reinforcement learning (DRL) algorithm, namely Mixed-Action Decision Intelligent Semantic-aware Soft-Actor Critic (MADIS-SAC), to handle the problem’s nonconvexity and NP-hard complexity. Results show 82.5% of QoSem improvement compared to baselines under resource constraints.

Multicast-based multimedia streaming is expected to be a key application in the next generation of wireless and mobile networks. For such applications, we utilize hierarchical non-terrestrial networks (NTNs) consisting of low earth orbit (LEO) satellites, high-altitude platforms (HAPs), and user equipment (UE). However, existing multicast protocols for NTN cause a long delay and jitter owing to packet loss, thus being the cause of low-quality streaming services. We propose a reliable multi-cast protocol, namely, overhearing and non-orthogonal multiple access (NOMA) repairing reliable multicast (ONRM), for delay reduction. In ONRM, the HAP overhears packets from the LEO to UE and retransmits the lost packets using NOMA on behalf of the LEO to prevent delay increments. We carried out evaluations based on the release of Third Generation Partnership Project (3GPP) in terms of delay, throughput, and jitter. We prepared existing multicast protocols, pragmatic general multi-cast (PGM), reliable and efficient multicast protocol (REMP), and extended bit-indexed explicit replication (extended BIER) for comparison. Results show that ONRM outperforms other multicast protocols. Especially, the proposed ONRM can realize a low jitter and enables high-quality multimedia streaming over NTN.

Device-to-device (D2D) communication has recently gained much attention for its potential to boost the capacity of cellular systems. D2D enables direct communication between devices while bypassing a base station (BS), hence decreasing the load of BSs and increasing the network throughput via spatial reuse of radio resources. However, the cellular system is highly dynamic, an optimal allocation plan of radio resource to D2D links at one time point can easily become suboptimal when devices move. Thus, to maximize spatial reuse in cellular systems, it is crucial to update the resource allocation adaptively to reflect the current system status. In this paper, we develop the first adaptive solution framework to the dynamic resource problem for maximizing spatial reuse. At the core of the framework, we present the two algorithms for the adaptive set multicover problem with approximation ratio f and log n, respectively, where f is the frequency of the most frequent element and n is the total number of elements. The experimental results not only show that our solutions have a significant improvement in running time, compared with optimal or approximated offline methods, but also demonstrate their good performance through the resource usage, network throughput, and other metrics.

Previous approaches to caching for Device-to-Device (D2D) communication cache popular files during off-peak hours. Since the popularity of content may evolve quickly or be unavailable in advance, we propose a flexible approach to cellular device caching where files are cached or uncached dynamically as file popularity evolves Dynamic caching motivates a space-efficient optimization problem Minimum File Placement (MFP), which is to cache a single file in the least amount of cache space to ensure a specified cache hit rate. In order to estimate the future cache hit rate, we use historical heterogeneous contact and request patterns of the devices. We present a bicriteria greedy algorithm for MFP and incorporate this algorithm into a dynamic approach to caching from a library of files with evolving popularity distribution. In an extensive experimental evaluation, we analyze the effectiveness of our approach to mobile device caching and demonstrate its advantages over other static contact-pattern-aware caching and alternative dynamic approaches.

Signaling networks are involved in almost all major diseases such as cancer. As a result of this, understanding how signaling networks function is vital for finding new treatments for many diseases. Using gene knockdown assays such as RNA interference (RNAi) technology, many genes involved in these networks can be identified. However, determining the interactions between these genes in the signaling networks using only experimental techniques is very challenging, as performing extensive experiments is very expensive and sometimes, even impractical. Construction of signaling networks from RNAi data using computational techniques have been proposed as an alternative way to solve this challenging problem. However, the earlier approaches are either not scalable to large scale networks, or their accuracy levels are not satisfactory. In this study, we integrate RNAi data given on a target network with multiple reference signaling networks and phylogenetic trees to construct the topology of the target signaling network. In our work, the network construction is considered as finding the minimum number of edit operations on given multiple reference networks, in which their contributions are weighted by their phylogenetic distances to the target network. The edit operations on the reference networks lead to a target network that satisfies the RNAi knockdown observations. Here, we propose two new reference-based signaling network construction methods that provide optimal results and scale well to large-scale signaling networks of hundreds of components. We compare the performance of these approaches to the state-of-the-art reference-based network construction method SiNeC on synthetic, semi-synthetic, and real datasets. Our analyses show that the proposed methods outperform SiNeC method in terms of accuracy. Furthermore, we show that our methods function well even if evolutionarily distant reference networks are used. Application of our methods to the Apoptosis and Wnt signaling pathways recovers the known protein-protein interactions and suggests additional relevant interactions that can be tested experimentally.

Motivated by online social networks that are linked together through overlapping users, we study the influence maximization problem on a multiplex, with each layer endowed with its own model of influence diffusion. This problem is a novel version of the influence maximization problem that necessitates new analysis incorporating the type of propagation on each layer of the multiplex. We identify a new property, generalized deterministic submodular, which when satisfied by the propagation in each layer, ensures that the propagation on the multiplex overall is submodular-for this case, we formulate influential seed finder (ISF), the greedy algorithm with approximation ratio (1-1/e). Since the size of a multiplex comprising multiple OSNs may encompass billions of users, we formulate an algorithm knapsack seeding of network (KSN) that runs on each layer of the multiplex in parallel. KSN takes an α-approximation algorithm A for the influence maximization problem on a single network as input, and has approximation ratio ((1 - ϵ)α)/((o + 1)k) for arbitrary ϵ > 0, o is the number of overlapping users, and k is the number of layers in the multiplex. Experiments on real and synthesized multiplexes validate the efficacy of the proposed algorithms for the problem of influence maximization in the heterogeneous multiplex. Implementations of ISF and KSN are available at http://www.alankuhnle.com/papers/mim/mim.html.

We consider the Threshold Activation Problem (TAP): given social network G and positive threshold T, find a minimum-size seed set A that can trigger expected activation of at least T. We introduce the first scalable, parallelizable algorithm with performance guarantee for TAP suitable for datasets with millions of nodes and edges; we exploit the bicriteria nature of solutions to TAP to allow the user to control the running time versus accuracy of our algorithm through a parameter α ϵ (0, 1): given η > 0, with probability 1 - η our algorithm returns a solution A with expected activation greater than T - 2αT, and the size of the solution A is within factor 1-h 4α/T + log(T) of the optimal size. The algorithm runs in time O (α-2log (n/η) (n + m)|A|), where n, m, refer to the number of nodes, edges in the network. The performance guarantee holds for the general triggering model of internal influence and also incorporates external influence, provided a certain condition is met on the cost-effectivity of seed selection.

Backscatter wireless communication (BWC) is a promising research area for the low power Internet of Things (IoT) devices. A substantial number of research works have been conducted in this area to increase the range of BWC. Along with physical layer achievements in BWC, an efficient medium access control (MAC) protocol is mandatory to attain the optimal benefit. In this paper, a novel MAC protocol is proposed to support BWC in a Wi-Fi network, where the AP and the Wi-Fi clients are capable of in-band full-duplex (IBFD) transmission, but the wireless tags (WTags) are half-duplex capable. In this MAC protocol, the AP suppresses the uplink transmission of the corresponding Wi-Fi client while transmitting the downlink data to support the BWC. Some new control frames have been introduced to facilitate this task. A mathematical analysis of this proposed MAC is presented here together with an evaluation of its performance. The mean overhead time is observed as 121 μs with the downlink utilization factor of 80%. The average throughput of the network decreases as the number of WTag increases. This average throughput is observed as 63.5 Mbps and 59 Mbps (with 30 WTags and 30 Wi-Fi clients) for the data delivery frequency of two times/WTag and eight times/WTag respectively.

Device-to-device (D2D) communications over the licensed wireless spectrum has been recently proposed as a promising technology to meet the capacity crunch of next generation cellular networks. However, due to the high mobility of cellular devices, establishing and ensuring the success of D2D transmission become a major challenge. To this end, in this paper, a novel framework is proposed to enable devices to form multi-hop D2D connections in an effort to maintain sustainable communication in the presence of device mobility. To solve the problem posed by device mobility, in contrast to existing works, which mostly focus on physical domain information, a durable community-based approach is introduced taking social encounters into context. It is shown that the proposed scheme can derive an optimal solution for time sensitive content transmission while also minimizing the cost that the base station pays in order to incentivize users to participate in D2D. Simulation results show that the proposed social community aware approach yields significant performance gain, in terms of the amount of traffic offloaded from the cellular network to the D2D tier, compared with the classical social-unaware methods.

Recent advancements in antenna design and signal processing have made it feasible to transmit and receive at the same time by using same carrier frequency, which is called in-band full-duplex (IBFD) wireless communications. Along with physical layer advancements, efficient medium access control (MAC) design is required to achieve optimum benefit from this latest technology. In this paper, a full-duplex MAC (FD-MAC) is proposed for a WLAN to support asymmetric length of traffic for uplink and downlink, which is named as Asym- MAC. The main feature of this MAC is that multiple users can transmit data to the access point (AP) during the transmission of a single downlink frame from AP. Moreover, there is no contention period and hence, there is no collision among the user terminals and AP for channel access. In Asym-MAC, AP always initiates the transmission. The performance of Asym-MAC is compared with the traditional half-duplex MAC (HD-MAC) and another proposed FD-MAC. Asym-MAC outperforms traditional HD-MAC by about 100% throughput gain. Moreover, this MAC shows higher throughput gain as compared to another existing FD-MAC and the throughput gain is about 67%. The results suggest that a significant throughput gain can be achieved by using this MAC, which can be used for future FD WLANs.

Opportunistic networks enable mobile devices to communicate with each other through routes that are built dynamically, while messages are en route between the sender and the destination(s). The social structure and interaction of users of such devices dictate the performance of routing protocols in those networks. Community structures, commonly exhibited by social networks, is also observed in the encounter patterns in opportunistic networks and has an astounding impact in designing forwarding algorithms for such types of networks. In this paper, we explore the structural vulnerability of social-based forwarding and routing methods in opportunistic networks. In particular, we introduce Community Vulnerability Assessment (CVA), a new problem on assessing the performance reliability of opportunistic routing strategies in Delay Tolerant Networks (DTN) from a community structure point of view. Given a positive number k, CVA aims to find out the k most vulnerable devices in the network whose non-participation (due to out-of-service or permanent out-of-range) transforms the current network community structure to a totally different one. As the first study in this direction, we analyze and provide key insights into the separation of network communities, evaluated via the Normalized Mutual Information (NMI). Based on these findings, we suggest an approximation algorithm for the special case when k 1/4 1, and a heuristic, genEdge, for the general case. To certify the effectiveness of our proposed approaches, we first test them on synthesized data with known community structures, and then we show the impact of node removal on community structures in real social networks. Finally we evaluate the performance via different forwarding and routing strategies in multiple real-world DTN traces. Our results indicate that, in many forwarding and routing methods, the nonparticipation of only some important devices is significant enough to degrade the entire network's performance.

Online social networks have become an effective and important social platform for communication, opinions exchange, and information sharing. However, they also make it possible for rapid and wide misinformation diffusion, which may lead to pernicious influences on individuals or society. Hence, it is extremely important and necessary to detect the misinformation propagation by placing monitors. In this article, we first define a general misinformation-detection problem for the case where the knowledge about misinformation sources is lacking, and show its equivalence to the influence-maximization problem in the reverse graph. Furthermore, considering node vulnerability, we aim to detect the misinformation reaching to a specific user. Therefore, we study a τ-Monitor Placement problem for cases where partial knowledge of misinformation sources is available and prove its # P complexity. We formulate a corresponding integer program, tackle exponential constraints, and propose a Minimum Monitor Set Construction (MMSC) algorithm, in which the cut-set 2 has been exploited in the estimation of reachability of node pairs. Moreover, we generalize the problem from a single target to multiple central nodes and propose another algorithm based on a Monte Carlo sampling technique. Extensive experiments on real-world networks show the effectiveness of proposed algorithms with respect to minimizing the number of monitors.

Online Social Networks (OSNs), such as Facebook, Twitter and Google+, facilitate the interactions and communications among people. However, they also make it a fertile land for misinformation to rapidly spread out, which may lead to detrimental consequences. Thus it is imperative to detect the misinformation propagating through OSNs by placing monitors. In this paper, we first study a general misinformation detection problem and show its equivalence to the influence maximization problem. Moreover, in order to prevent misinformation from reaching specific users, we define a τ-Monitor Placement problem for cases where the partial knowledge of misinformation sources is available. We prove the #P complexity of this problem and additionally propose an efficient algorithm to solve it. Extensive experiments on real-world data show the effectiveness of our proposed algorithm with respect to minimizing the number of monitors.

Many complex systems, from World Wide Web and online social networks to mobile networks, exhibit community structure in which nodes can be grouped into densely interconnected communities. This special structure has been exploited extensively to design better solutions for many operations and applications such as routing in wireless networks, worm containment and interest prediction in social networks. The outcome of these solutions are sensitive to the network structures, which raises an important question: can communities be broken easily in a network? To answer this question, we introduce a density-based problem formulation for analyzing the vulnerability of communities. Our approach includes the NP-completeness and a O(log k) approximation algorithm for solving the problem where k is the number of communities to be broken. Additionally, we analyze the vulnerability of communities in the context of arbitrary community detection algorithms. The empirical results show that communities are vulnerable to edge removal and in some cases the removal of a small fraction of edges can break the community structure.

We introduce Community structure Vulnerability Assessment (CVA) problem to assess the network vulnerability under a community structure point of view. Given a positive number k, CVA aims to find out the k most vulnerable nodes whose removals maximally transform the current network community structure to a different one. As the first attempt, we suggest an approximation algorithm for the special case k = 1, and propose multiple greedy algorithms for CVA problem. To certify the effectiveness of suggested approaches, we test them on not only synthesized networks with known community structures but also on real-world social traces.