Approximating optimal Broadcast in Wireless Mesh Networks with Machine Learning

With the growth of IoT, efficient broadcast is required for many applications. Yet, current protocols use primitive mechanisms based on heuristics. Multi-agent reinforcement learning is applied to approximate optimal broadcast in Wireless Mesh Networks. One of the proposed fully distributed algorithms, using Bayesian Neural Networks, outperforms MORE multicast and BATMAN, improving airtime up to 20%, e2e delay up to 30%, and satisfying timeout constraints in over the 97% of the cases

Cross-layer schemes for performance optimization in wireless networks

Wireless networks are undergoing rapid progress and inspiring numerous applications. As the application of wireless networks becomes broader, they are expected to not only provide ubiquitous connectivity, but also support end users with certain service guarantees. End-to-end delay is an important Quality of Service (QoS) metric in multihop wireless networks. This dissertation addresses how to minimize end-to-end delay through joint optimization of network layer routing and link layer scheduling. Two cross-layer schemes, a loosely coupled cross-layer scheme and a tightly coupled cross-layer scheme, are proposed. The two cross-layer schemes involve interference modeling in multihop wireless networks with omnidirectional antenna. In addition, based on the interference model, multicast schedules are optimized to minimize the total end-to-end delay. Throughput is another important QoS metric in wireless networks. This dissertation addresses how to leverage the spatial multiplexing function of MIMO links to improve wireless network throughput. Wireless interference modeling of a half-duplex MIMO node is presented. Based on the interference model, routing, spatial multiplexing, and scheduling are jointly considered in one optimization model. The throughput optimization problem is first addressed in constant bit rate networks and then in variable bit rate networks. In a variable data rate network, transmitters can use adaptive coding and modulation schemes to change their data rates so that the data rates are supported by the Signal to Noise and Interference Ratio (SINR). The problem of achieving maximum throughput in a millimeter-wave wireless personal area network is studied --Abstract, page iv

In-Network Processing For Mission-Criticalwireless Networked Sensing And Control: A Real-Time, Efficiency, And Resiliency Perspective

As wireless cyber-physical systems (WCPS) are increasingly being deployed in mission-critical applications, it becomes imperative that we consider application QoS requirements in in-network processing (INP). In this dissertation, we explore the potentials of two INP methods, packet packing and network coding, on improving network performance while satisfying application QoS requirements. We find that not only can these two techniques increase the energy efficiency, reliability, and throughput of WCPS while satisfying QoS requirements of applications in a relatively static environment, but also they can provide low cost proactive protection against transient node failures in a more dynamic wireless environment. We first study the problem of jointly optimizing packet packing and the timeliness of data delivery. We identify the conditions under which the problem is strong NP-hard, and we find that the problem complexity heavily depends on aggregation constraints instead of network and traffic properties. For cases when the problem is NP-hard, we show that there is no polynomial-time approximation scheme (PTAS); for cases when the problem can be solved in polynomial time, we design polynomial time, offline algorithms for finding the optimal packet packing schemes. We design a distributed, online protocol tPack that schedules packet transmissions to maximize the local utility of packet packing at each node. We evaluate the properties of tPack in NetEye testbed. We find that jointly optimizing data delivery timeliness and packet packing and considering real-world aggregation constraints significantly improve network performance. We then work on the problem of minimizing the transmission cost of network coding based routing in sensor networks. We propose the first mathematical framework so far as we know on how to theoretically compute the expected transmission cost of NC-based routing in terms of expected number of transmission. Based on this framework, we design a polynomial-time greedy algorithm for forwarder set selection and prove its optimality on transmission cost minimization. We designed EENCR, an energy-efficient NC-based routing protocol that implement our forwarder set selection algorithm to minimize the overall transmission cost. Through comparative study on EENCR and other state-of-the-art routing protocols, we show that EENCR significantly outperforms CTP, MORE and CodeOR in delivery reliability, delivery cost and network goodput. Furthermore, we study the 1+1 proactive protection problem using network coding. We show that even under a simplified setting, finding two node-disjoint routing braids with minimal total cost is NP-hard. We then design a heuristic algorithm to construct two node-disjoint braids with a transmission cost upper bounded by two shortest node-disjoint paths. And we design ProNCP, a proactive NC-based protection protocol using similar design philosophy as in EENCR. We evaluate the performance of ProNCP under various transient network failure scenarios. Experiment results show that ProNCP is resilient to various network failure scenarios and provides a state performance in terms of reliability, delivery cost and goodput. Our findings in this dissertation explore the challenges, benefits and solutions in designing real-time, efficient, resilient and QoS-guaranteed wireless cyber-physical systems, and our solutions shed lights for future research on related topics

Recent Advances in Cellular D2D Communications

Device-to-device (D2D) communications have attracted a great deal of attention from researchers in recent years. It is a promising technique for offloading local traffic from cellular base stations by allowing local devices, in physical proximity, to communicate directly with each other. Furthermore, through relaying, D2D is also a promising approach to enhancing service coverage at cell edges or in black spots. However, there are many challenges to realizing the full benefits of D2D. For one, minimizing the interference between legacy cellular and D2D users operating in underlay mode is still an active research issue. With the 5th generation (5G) communication systems expected to be the main data carrier for the Internet-of-Things (IoT) paradigm, the potential role of D2D and its scalability to support massive IoT devices and their machine-centric (as opposed to human-centric) communications need to be investigated. New challenges have also arisen from new enabling technologies for D2D communications, such as non-orthogonal multiple access (NOMA) and blockchain technologies, which call for new solutions to be proposed. This edited book presents a collection of ten chapters, including one review and nine original research works on addressing many of the aforementioned challenges and beyond