Dynamicity is one of the most challenging, yet, central aspects of wireless networks. Dynamicity can come in many guises, such as churn (node insertion/deletion) and node mobility. Although the study of dynamic networks has been popular in distributed computing domain, previous works considered only partial factors causing dynamicity. We here propose a dynamic model that is comprehensive to include crucial dynamic factors on nodes and links. Our model defines dynamicity in terms of localized topological changes in the vicinity of each node, rather than a global view of the whole network. Obviously, a localized dynamic model suits distributed algorithm studies better than a global one. The proposed dynamic model uses the more realistic SINR model to describe wireless interference, instead of the oversimplified graph-based models adopted in most existing works. Under the proposed dynamic model, we provide an efficient distributed algorithm accomplishing local broadcast services in the abstract MAC layer that was first presented by Kuhn et al.\ \cite{DBLP:conf/wdag/KuhnLN09}. Our solution paves the road for many new fast algorithms for solving high-level problems in dynamic networks, such as consensus, single-message broadcast, and multiple-message broadcast. Extensive simulations show that our algorithm exhibits good performance in realistic environments with dynamic behaviors.

The byzantine model is widely used to depict a variety of node faults in networks. Previous studies on byzantine-resilient protocols in wireless networks assume reliable communications and do not consider the jamming behavior of byzantine nodes. Such jamming, however, is a very critical and realistic behavior to be considered in modern wireless networks. In this paper, for the first time, we integrate the jamming behavior of byzantine nodes into the network setting. We show that, in this much more comprehensive and harsh model, efficient distributed communication protocols can be still devised with elaborate protocol design. In particular, we developed an algorithm that can accomplish the basic multiple-message dissemination task close to the optimal solution in terms of running time. Empirical results validate the byzantine-resilience and efficiency of our algorithm.

Acoustic sensing for heartbeat monitoring has become a prevailing research topic in wireless sensing. Existing acoustic sensing systems have two limitations---limited sensing range, and heartbeat monitoring for a single user only, hindering the large-scale deployment of applications. In this paper, we present DF-Sense, a Dual Forming based multi-user acoustic Sensing system for heartbeat monitoring in home settings. Specifically, we design a novel sensing signal-to-noise ratio (SSNR) enhancement model, namely Dualforming, based on the constructive superposition across multiple subcarriers and microphones, and further build the quantitative relationship between critical factors and SSNR enhancement to optimize sensing performance. To enable Dualforming, we propose a novel MUltiple Subtle SIgnal Classification (MUS²IC) method to identify multiple subjects with subtle motions. We implement DF-Sense using commercial acoustic devices and conduct extensive experiments in a home setting. Results show that DF-Sense achieves high precision measurement of instantaneous heart rate within the range of 10 m, which is sufficient for most daily space requirements, and is able to monitor heartbeat for up to 6 subjects in a 2-D space simultaneously.

In the past decades, Dynamic Internet of Things (D-IoT) networks have played a conspicuously more important role in many real-life areas, including disaster relief, environment monitoring, public safety, and so on, to rapidly collect information from the environment and help people to make the decision. Meanwhile, due to the widespread implementation of dynamic IoT networks, there exists an enormous demand on designing suitable models and efficient algorithms for fundamental operations in dynamic IoT networks, to achieve the high throughput and reliable low-latency communication demands in 6G networks. In this article, we first present a general dynamic model to comprehensively depict most of the dynamic phenomena in IoT networks. Then, based on the proposed dynamic model, a distributed scheduling algorithm is proposed to competitively optimize the Age-of-Information (AoI) problem in the context of a D-IoT network. We say our scheduling algorithm is competitive: the throughput of the base station approximates the optimal solution with constant competitive ratio; and, the latency for a packet received by the base station is only constant times larger than the optimal latency. Rigorous theoretical analysis and extensive simulations are presented to verify the high throughput and reliable low-latency communications in our proposed algorithm.

The operations on the aircraft carrier flight deck are carried out in a time‐critical and resource‐constrained environment with uncertainty, and it is of great significance to optimize the makespan and obtain a robust schedule and resource allocation plan for a greater sortie generation capacity and better operational management of an aircraft carrier. In this paper, a proactive robust optimization method for flight deck scheduling with stochastic operation durations is proposed. Firstly, an operation on node‐flow (OONF) network is adopted to model the precedence relationships of multi‐aircraft operations, and resource constraints categorized into personnel, support equipment, workstation space, and supply resource are taken into consideration. On this basis, a mathematical model of the robust scheduling problem for flight deck operation (RSPFDO) is established, and the goal is to maximize the probability of completing within the limitative makespan (PCLM) and minimize the weighted sum of expected makespan and variance of makespan (IRM). Then, in terms of proactive planning, both serial and parallel schedule generation schemes for baseline schedule and robust personnel allocation scheme and equipment allocation adjustment scheme for resource allocation are designed. In terms of executing schedules, an RSPFDO‐oriented preconstraint scheduling policy (C^PC) is proposed. To optimize the baseline schedule and resource allocation, a hybrid teaching‐learning‐based optimization (HTLBO) algorithm is designed which integrates differential evolution operators, peak crossover operator, and learning‐automata‐based adaptive variable neighborhood search strategy. Simulation results shows that the HTLBO algorithm outperforms both some other state‐of‐the‐art algorithms for deterministic cases and some existing algorithms for stochastic project scheduling, and the robustness of the flight deck operations can be improved with the proposed resource allocation schemes and C^PC policy.

In this paper, we studied a data caching problem in edge-based CDNs to facilitate the content delivery to serve a sequence of requests, off-line and online, with minimum costs as a goal based on a semi-homo cost model. To this end, we first designed an O(mn \log(mn)) time and space optimal proactive off-line algorithm,called pro-caching, by reducing the problem to a simple shortest path problem in a directed weighted network graph, and then extended the idea of anticipatory caching to develop an 2-competitive reactive online algorithm, called re-caching, for this problem and showed its tightness by proving that no deterministic online algorithm can do better than 2-o(1) in its worst case. Finally, to combine the advantages of both algorithms, we also presented a hybrid algorithm, called hy-caching, to fully utilize the power and benefits of edge-based CDNs while reducing their service costs. Our results improve the previous results not only in the cost model being used but also in the time complexity, competitive ratio, and the quality of the solutions. We provably achieve these results with our deep insights into the problem and the careful analysis, together with an empirical evaluation.