The classic multi-hop wireless network optimization problem has recently re-attracted many attentions due to some emerging applications such as wireless mesh network, space-air ground integrated networks, and 5G/6G integrated access and backhaul systems. The key issue in multi-hop wireless network optimization is the interference management through scheduling. The fundamental NP-hardness of this problem is that there are exponentially many possible independent sets (ISs), but only a small number of them will be scheduled in the optimal solution (termed as the scheduling structure). The existing literature on approximation algorithms are mainly along the direction of searching the ISs in a heuristic manner. With the capability of machine learning (ML) algorithms in supporting big data analytics, in this paper, we propose a two-stage self-supervised learning framework that can explore the scheduling structure from historical optimization instances, and such knowledge is then exploited to solve a new instance with greatly reduced computational overhead. At the exploring stage, we develop dimension reduction techniques for effective scheduling structure classification in a high-dimensional vector space. At the exploiting stage, we design an innovative structure criticalness indicator (SCI)-based IS selection algorithm that can robustly lead to close-to-optimal approximation of the average achievable throughput with roughly constant complexity. Furthermore, we also contribute a geometric canonical representation (GCR) system to equip the ML-assisted optimization framework with a topology-aware applicability. The proposed topology-aware ML (TAML) framework exhibits good generalizability across different network topologies and flow demands.

Age of information (AoI) is a crucial metric in modern communication systems, quantifying the information freshness at the receiver side. This study proposes a novel and general approach utilizing stochastic hybrid systems (SHS) for AoI analysis and minimization in carrier sense multiple access (CSMA) networks. Specifically, we consider a practical networking scenario where multiple nodes contend for transmission through a standard CSMA-based medium access control (MAC) protocol, and the tagged node under consideration uses a small transmission buffer for a low AoI. We for the first time develop an SHS-based analytical model for this finite-buffer transmission system over the CSMA MAC. Moreover, we develop a creative method to incorporate the collision probability into the SHS model, with background nodes having heterogeneous traffic arrival rates. This new model enables us to analytically find the optimal sampling rate to minimize the AoI of the tagged node in a wide range of practical networking scenarios. Our analysis reveals insights into buffer size impacts when jointly optimizing throughput and AoI. The SHS model is cast over an 802.11-based MAC to examine the performance, with comparison to ns-based simulation results. The accuracy of the modeling and the efficiency of optimal sampling are convincingly demonstrated.

Unmanned aerial vehicles (UAVs) are being widely exploited for various applications,e.g., traversing to collect data from ground sensors, patrolling to monitor key facilities, moving to aid mobile edge computing. We summarize these UAV applications and formulate a problem, namely thegeneral waypoint-based PoI-visiting problem. Since energy is critical due to the limited onboard storage capacity, we aim at minimizing flight energy consumption. In our problem, we pay special attention to the energy consumption for turning and switching operations on flight planning, which are usually ignored in the literature but play an important role in practical UAV flights according to our real-world measurement experiments. We propose specially designed graph parts to model the turning and switching cost and thus transfer the problem into a classic graph problem,i.e., general traveling salesman problem, which can be efficiently solved. Theoretical analysis shows that such problem transformation has the graph redefinition approximation ratio upper bound,$max\lbrace \Theta /\delta ,2\rbrace$, where$\Theta$is related to the designed graph parts and$\delta$is a constant. Finally, we evaluate our proposed algorithm by simulations. The results show that it costs less than 107% of the optimal minimum energy consumption for small scale problems and costs only 50% as much energy as a naive algorithm for large scale problems.

In this paper, we study how to minimize the age of information (AoI) for remote monitoring over a WiFi network, where a tagged node under study needs to deliver the sampling messages to the monitoring application installed at the access point (AP). We consider a very challenging practical scenario where multiple background nodes might incorporate heterogeneous and generic traffic models; all the nodes contend for the transmission channel through the practical IEEE 802.11 based medium access control (MAC) protocol. The existing AoI analyses over distributed MAC protocol are not sufficient for our problem, which are limited to simplified MAC modeling or homogeneous traffic modeling. We propose an AoI optimization algorithm that integrates the AoI queueing analysis with the 802.11 MAC performance analysis. Specifically, we develop an innovative method to address the impact of the MAC channel attention on the message service time of the tagged node and compute the minimal AoI iteratively. Simulation results demonstrate that our algorithm is very accurate and robust crossing a variety of networking scenarios. Moreover, our methods require only local computation and slight probing of the conditional collision probability, making them suitable for practical use.

Chainweb and some other parallel blockchain systems have recently been proposed, with the objectives of improving the throughput and enhancing the tamper-proof capability. While many security related studies have been conducted for traditional single-chain based blockchain systems, the security aspect of parallel chain systems is yet to be well studied and understood. Our paper presents a systematic study on selfish mining attacks in Chainweb based on mathematical modeling. Specifically, selfish mining is conducted by concentrating the computation power on a subset of parallel chains and operating a proper withholding strategy. We demonstrate how to establish a Markov chain based analytical model with innovative techniques to handle the very large state space. Our Markov chain model is also capable of handling different number of parallel chains. The mathematical analysis brings an insightful, in fact counterintuitive, finding that the attackers need less computation power to harvest additional rewards through withholding when Chainweb contains a larger number of chains; while the common understanding is that the more chains are used, the more tamper-proof the system is. The accuracy of the Markov chain analysis is demonstrated via comparison to the simulation results.

It is well known that the Proof-of-Work (PoW) based blockchain scheme, first introduced in Bitcoin system, is not scalable, where the PoW implementation limits the transaction processing rate. In recent years, parallel chain techniques have been proposed to overcome this issue. Chainweb is one of the parallel chains to be studied in this paper with a focus on security related issues. It is worth noting that existing blockchain security studies mainly focus on traditional single-chain based protocols. There are not many security related studies on parallel blockchains. This paper for the first time reveals that selfish mining attack is possible on Chainweb blockchain, to the best of our knowledge. Specifically, we propose a selfish mining attack that exclusively mines blocks on a subset of parallel chains with the same block height and achieves gain through a proper withholding strategy. We develop a mathematical model to quantitatively evaluate the performance and demonstrate the effectiveness of the proposed attack. Our results show that the attacker can gain extra mining reward when his computational power is at least 38% of the total power in Chainweb network. Under the 50% computational power restriction, the attacker's extra gain increases monotonically with its computational power.

Wireless optimization involves repeatedly solving difficult optimization problems, and data-driven deep learning techniques have great promise to alleviate this issue through its pattern matching capability: past optimal solutions can be used as the training data in a supervised learning paradigm so that the neural network can generate an approximate solution using a fraction of the computational cost, due to its high representing power and parallel implementation. However, making this approach practical in networking scenarios requires careful, domain-specific consideration, currently lacking in similar works. In this paper, we use deep learning in a wireless network scheduling and routing to predict if subsets of the network links are going to be used, so that the effective problem scale is reduced. A real-world concern is the varying data importance: training samples are not equally important due to class imbalance or different label quality. To compensate for this fact, we develop an adaptive sample weighting scheme which dynamically weights the batch samples in the training process. In addition, we design a novel loss function that uses additional network-layer feature information to improve the solution quality. We also discuss a post-processing step that gives a good threshold value to balance the trade-off between prediction quality and problem scale reduction. By numerical simulations, we demonstrate that these measures improve both the prediction quality and scale reduction when training from data of varied importance.