Vector control has been a key component in the fight against malaria for decades, and chemical insecticides are critical to the success of vector control programs worldwide. However, increasing resistance to insecticides threatens to undermine these efforts. Understanding the evolution and propagation of resistance is thus imperative to mitigating loss of intervention effectiveness. Additionally, accelerated research and development of new tools that can be deployed alongside existing vector control strategies is key to eradicating malaria in the near future. Methods such as gene drives that aim to genetically modify large mosquito populations in the wild to either render them refractory to malaria or impair their reproduction may prove invaluable tools. Mathematical models of gene flow in populations, which is the transfer of genetic information from one population to another through migration, can offer invaluable insight into the behavior and potential impact of gene drives as well as the spread of insecticide resistance in the wild. Here, we present the first multi-locus, agent-based model of vector genetics that accounts for mutations and a many-to-many mapping cardinality of genotypes to phenotypes to investigate gene flow, and the propagation of gene drives in Anopheline populations. This model is embedded within a large scale individual-based model of malaria transmission representative of a high burden, high transmission setting characteristic of the Sahel. Results are presented for the selection of insecticide-resistant vectors and the spread of resistance through repeated deployment of insecticide treated nets (ITNs), in addition to scenarios where gene drives act in concert with existing vector control tools such as ITNs. The roles of seasonality, spatial distribution of vector habitat and feed sites, and existing vector control in propagating alleles that confer phenotypic traits via gene drives that result in reduced transmission are explored. The ability to model a spectrum of vector species with different genotypes and phenotypes in the context of malaria transmission allows us to test deployment strategies for existing interventions that reduce the deleterious effects of resistance and allows exploration of the impact of new tools being proposed or developed.

As more regions approach malaria elimination, understanding how different interventions interact to reduce transmission becomes critical. The Lake Kariba area of Southern Province, Zambia, is part of a multi-country elimination effort and presents a particular challenge as it is an interconnected region of variable transmission intensities. In 2012-13, six rounds of mass test-and-treat drug campaigns were carried out in the Lake Kariba region. A spatial dynamical model of malaria transmission in the Lake Kariba area, with transmission and climate modeled at the village scale, was calibrated to the 2012-13 prevalence survey data, with case management rates, insecticide-treated net usage, and drug campaign coverage informed by surveillance. The model captured the spatio-temporal trends of decline and rebound in malaria prevalence in 2012-13 at the village scale. Various interventions implemented between 2016-22 were simulated to compare their effects on reducing regional transmission and achieving and maintaining elimination through 2030. Simulations predict that elimination requires sustaining high coverage with vector control over several years. When vector control measures are well-implemented, targeted mass drug campaigns in high-burden areas further increase the likelihood of elimination, although drug campaigns cannot compensate for insufficient vector control. If infections are regularly imported from outside the region into highly receptive areas, vector control must be maintained within the region until importations cease. Elimination in the Lake Kariba region is possible, although human movement both within and from outside the region risk damaging the success of elimination programs.

Given n source nodes and k relay nodes, we model the optimal relay topology problem allowing for simultaneous optimization of the relay node locations and traffic through the network, so that the overall number of packet retransmissions is minimized. We argue that state-of-the-art models and algorithms for relay placement in wireless networks do not reflect salient characteristics of the optimal relays topology and lead to suboptimal solutions. We do not constrain the position of relays to a finite set of discrete points, as the latter may not be feasible in practical networks. In this case, we show that just listing a set of feasible sites for the relays is already at least APX-hard. Exploiting convexity in a special case of the network communication cost function, we give an optimal algorithm for the relay placement problem. However, the algorithm is exponential on the number of nodes in the network. We suggest a practical heuristic algorithm for relay placement: RePlace. We compare RePlace numerically to the optimal algorithm and show that RePlace achieves the optimal or almost optimal solutions. We implement RePlace in the full network stack simulator JiST/SWANS. The relay topologies generated by RePlace eliminate overhead communication cost almost entirely.

Broadcast is a fundamental operation in networks, especially in reconfigurable wireless ad hoc networks. For example, some form of broadcasting is used by all on-demand mobile networks routing protocols, when there is uncertainty as to the location of the destination node, or for service discovery. In this work, we present a new approach to efficient broadcast in networks with dynamic topologies, and we introduce the time sequence scheme (TSS), a new online local broadcasting algorithm for such networking environments. TSS ranks by priority, in a distributed way, candidate broadcasting nodes so that the overall number of re-broadcasts in the network is minimized. We evaluate TSS, showing that its performance comes remarkably close to the corresponding theoretical performance bounds, even in the presence of packet loss due, for example, to MAC-layer collisions. Furthermore, we compare our algorithm with a number of recently proposed schemes considering their performance in various realistic network mobility scenarios. We demonstrate that TSS performance is robust in the context of mobility induced topology reconfigurations-including temporal network partitioning-during propagation of the broadcast message.

Broadcast is a fundamental operation in networks, especially in wireless Mobile Ad Hoc NETworks (MANET). For example, some form of broadcasting is used by all on-demand MANET routing protocols, when there is uncertainty as to the location of the destination node, or for service discovery. Being such a basic operation of the networking protocols, the importance of efficient broadcasting has long been recognized by the networking community. Numerous papers proposed increasingly more efficient implementation of broadcasting, while other studies presented bounds on broadcast performance. In this work, we present a new approach to efficient broadcast in networks with dynamic topologies, such as MANET, and we introduce a new broadcasting algorithm for such networking environments. We evaluate our algorithm, showing that its performance comes remarkably close to the corresponding theoretical performance bounds. Furthermore, we compare the performance of the proposed algorithm with other recently proposed schemes, especially in the context of various mobility settings.

Application-aware routing exploits static knowledge of an application's traffic pattern to improve performance compared to generalpurpose routing algorithms. Unfortunately, traditional approaches to application-aware routing cannot efficiently handle dynamic changes in the traffic pattern limiting its usefulness in practice. In this paper, we study application-aware routing under traffic uncertainty. Our problem formulation allows an application to statically specify an uncertainty set of traffic patterns that each occur with a given probability, and our goal is to find a single set of combined routes that will enable high-performance across all of these traffic patterns. We show how efficient combined routes can be found for this problem using convex optimization. These combined routes are optimal when the performance for every traffic pattern using the combined routes is the same as the performance using routes that are specialized for just that traffic pattern. We derive necessary and sufficient conditions for when our optimization framework will find optimal combined routes. We use theoretical and numerical analysis for the important class of permutation traffic patterns to quantify how often optimal combined routes exist and to determine the performance loss when optimal combined routes are infeasible. Finally, we use a cycle-level simulator that includes realistic pipeline latencies, arbitration, and buffered flow-control to study the latency and throughput of combined routes compared to specialized routes and routes generated using general-purpose routing algorithms. The theoretical analysis, numerical analysis, and simulation results in this paper provide a first step towards more flexible application-aware routing.