The spread of infectious diseases in networked populations poses significant challenges for public health intervention strategies. Traditional centrality-based and heuristic network dismantling approaches prioritize highly connected nodes but often fail to account for individual mortality risk, limiting their effectiveness in minimizing overall fatalities. While recent advances in machine learning have improved intervention strategies, existing models largely focus on reducing disease transmission rather than directly targeting mortality outcomes. To address this gap, we propose a reinforcement learning-based framework that integrates graph representation learning to identify and remove high-risk nodes, thereby maximizing network fragmentation while minimizing overall deaths. The framework is trained using synthetic networks and evaluated on five synthetic and four real-world datasets, benchmarking its performance against state-of-the-art network dismantling methods [graph dismantling with machine learning (GDM), generalized network dismantling (GND), and graph enhanced reinforcement learning (GERL)]. Experimental results demonstrate that the proposed method consistently outperforms baseline approaches, particularly in scale-free and community-structured networks, where targeted node removal significantly weakens network connectivity and suppresses epidemic spread. Moreover, in real-world networks, the method achieves lower cumulative death rates and higher epidemic thresholds, highlighting its robustness in controlling disease propagation. By incorporating mortality risk into network representation learning, the proposed framework offers a scalable, adaptive, and socially responsible approach to epidemic mitigation, misinformation control, and network resilience enhancement.

Activity detection plays an important role in grantfree random access, a promising approach for handling a large number of users in use cases like massive machine type communication (mMTC). Existing activity detection algorithms cover various scenarios but overlook wideband distributed antenna systems, a practical configuration for next-generation wireless networks. When following conventional activity detection methods, sparse Bayesian learning (SBL) could be an option in this case. However, SBL-based methods for wideband systems lack the consistency of maximum likelihood estimation (MLE), resulting in unsatisfactory detection performance. This paper proposes a novel distributed activity detection framework for wideband cell-free multiple-input and multiple-output (MIMO). Specifically, we provide a novel uplink channel model for activity detection in wideband cell-free MIMO, accounting for asynchronous reception. Additionally, we present possible SBLbased methods, identifying their limitations, which motivates the development of a new approach for activity detection. Next, we propose a covariance-matching distributed activity detection framework that matches the sample covariance matrix to the estimated covariance matrix. Simulation results demonstrate the effectiveness of the proposed distributed algorithm.

One of the Sustainable Development Goals of the United Nations is affordable and clean energy. True utilization of renewable energy is only possible via battery-based energy storage systems. Overseeing the operation of battery-based energy storage systems and diagnosing abnormal batteries are of the utmost importance for their durability and stability. Because of inadequate anomalous samples and privacy considerations, we jointly train a global autoencoder on various battery-based energy storage systems to detect anomalous batteries. Due to potentially unstable network connectivity in energy storage systems, a chunk of model parameters may be lost during model transmission, leading to dramatic performance deterioration. The trained model tends to classify all measurements as anomalies. To solve this problem, we propose a robust federated learning scheme to mitigate negative impact caused by packet loss during model transmission. By permuting and unpermuting model parameters before and after model transmission, we are able to distribute the lost parameters across the entire model. Such a loss can no longer have a significant negative impact on anomalous battery detection. Experimental results illustrate that the proposed algorithm is robust against packet loss during the model exchange between the cloud server and battery-based energy storage systems.

Extreme Edge Computing (EEC) can drastically curtail the delay, reduce network bandwidth consumption, and enhance system performance by providing computing resources closer to the data-generating Internet of Things (IoT) devices. However, the use of Extreme Edge Devices (EEDs) in EEC presents unique challenges imposed by the inherent dynamic user-access behavior, which introduces highly dynamic resource usage. To tackle such challenges, it is crucial to enable accurate resource usage predictions, which in turn requires having reliable datasets. In this paper, we cultivate the Dynamic Resource Usage Data Generation for EEDs (DRUDGE) methodology. DRUDGE generates datasets that capture the resource usage dynamics of EEDs running diverse user-end applications in fine-grained intervals over extended periods. We present an in-depth characterization of resource utilization in EEDs and make the datasets publicly available to the research community. We examine the temporal variation of critical system metrics, such as CPU usage, memory usage, temperature, and network traffic. Furthermore, we apply various statistical tests to gain valuable insights into the data characteristics, including skewness, kurtosis, stationarity, volatility, cointegration, multi-collinearity, Granger causality, and Pearson correlation analysis. These insights inform model selection, feature engineering, and preprocessing techniques, leading to more accurate and reliable forecasts and analyses for EEC systems.

We have aspired as a green and intelligent future, where humans, the built environment, and the nature are interconnected as a cyber-physical system. To such an Internet of Things, the sustainability and robustness of the power system is crucial, and the reliable operation of the battery-backed energy storage systems is key because of their abilities in power smoothing and shifting. However, detecting battery failures at the early-deployment stage is challenging due to the unavailability of anomalous measurement data and privacy concerns. In this paper, we propose an anomaly detection scheme for the energy storage systems without using prior information. We train autoencoders on the normal measurement data. Instead of training autoencoders in a centralized way, we train a global autoencoder over many energy storage systems in a federated manner without compromising privacy. Experimental results show that the proposed algorithm effectively detects anomalous batteries instantly after the system is set up without sharing sensitive data.

Democratizing the edge by leveraging the prolific yet underutilized computational resources of end devices, referred to as Extreme Edge Devices (EEDs), can open a new edge computing tech market that is people-owned, democratically managed, and accessible/lucrative to all. Parallel computing at EEDs can also move the computing service much closer to end-users, which can help satisfy the stringent Quality-of-Service (QoS) requirements of delay-critical and/or data-intensive IoT applications. However, EEDs are heterogeneous user-owned devices, and are thus subject to a highly dynamic user access behavior (i.e., dynamic resource usage). This makes the process of determining the computational capability of EEDs increasingly challenging. Estimating the dynamic resource usage of EEDs (i.e., workers) has been mostly overlooked. The complexity of Machine Learning (ML)-based models renders them impractical for deployment at the edge for the purpose of such estimations. In this paper, we propose the Resource Usage Multi-step Prediction (RUMP) scheme to estimate the dynamic resource usage of workers over multiple steps ahead in a computationally efficient way while providing a relatively high prediction accuracy. Towards that end, RUMP exploits the use of the Hierarchical Dirichlet Process-Hidden Semi-Markov Model (HDP-HSMM) to estimate the dynamic resource usage of workers in EED-based computing paradigms. Extensive evaluations on a real testbed of heterogeneous workers for multi-step sizes show an 87.5% prediction accuracy for the starting point of 2-steps and coming to as little as a 16% average difference in prediction error compared to a representative of state-of-the-art ML-based schemes.

Massive multiple-input-multiple-output (MIMO) and millimeter wave have been adopted as the enabling technologies for the 5G and beyond 5G (B5G) systems. A challenging problem introduced by the use of large antenna size and wide bandwidth is beam squint, i.e., spatial-wideband effect. Beam squint can significantly degrade the channel estimation performance for conventional channel estimators. Research effort on channel estimation under beam squint conditions has been very limited. For the few available work that attempts to address this problem, they require either all subcarriers or multiple symbols used as pilot for channel estimation, so large overhead becomes inevitable. Therefore, in this paper, we propose an efficient channel estimation method that only requires a small number of subcarriers. The channel estimation problem is formulated as a nonlinear least squares optimization problem. Initial parameter estimation is critical, which will affect the efficiency and convergence of the proposed algorithm. Using a densely-spaced antenna structure and consecutive subcarriers assignment approach, we can effectively avoid the aliasing effect and reduce the ambiguity during the initialization phase. A subcarrier assignment criterion is proposed to achieve the optimal performance. Closed-form expressions of the Cramér-Rao lower bound (CRLB) and the achievable rate are derived to evaluate the performance. Both simulation results and theoretical analysis show that even with a small number of subcarriers, the estimation error closely approaches the CRLB, and its effect is negligible compared with the noise when evaluating the signal-to-noise ratio with a simple linear detector. Furthermore, the number of pilot subcarriers has little impact on the achievable rate.

Many human diseases can be attributed to genetic variations in the genome. Scientists have been identifying genetic variants associated with disease risks using population-based data. With this knowledge, an individual's genetic liability to a disease can be estimated using the polygenic risk score (PRS), calculated based on their genotype profile. However, selecting the most predictive genetic variants is challenged by the high dimensionality of genomics data. Typically, hundreds of thousands of genetic variants are being tested on their association with a disease risk. Moreover, the effect of a genetic variant on a disease risk is often influenced by other variants. It is their interactions that contribute to a disease risk. In this research, we propose a feature selection method for PRS assessment that is able to search for combinations of genetic variants using a genetic algorithm and network science. Our method provides accurate predictive models for PRS computation, as well as useful insights into the intertwined relationships among a large number of genetic variants.

The universal presence of networks makes them an important conduit to study interactions in complex natural and artificial systems. While maintaining their integrity is crucial, in many cases, we are also interested in disconnecting them for disease prevention and control, failure containment, crime disruption, etc. With an array of methods exploring node importance, localized execution, and measurement of fragmentation, the choices we have can be disorienting. In this work, we propose a general framework, DisNet, in order to investigate the choice of node centralities and how distributed information gathering and decision making can help us achieve the balance between efficacy and cost of doing so. The framework was evaluated using computer simulation of network dissolution for the full process of weakening, breaking, and shattering. Measurements of focus include the structural losses such as increased effective diameter, homogenization of node degrees, and Shannon diversity of resultant network fragments.

We may inadvertently forget the time dimension or one common element in studying time-labeled data of various complex systems. Using temporal network modeling in an attempt to fill the gap in the complex network analysis, we explore people interactions via analyzing mobile phone data in the quest of finding the average ratio of people that an individual can connect or influence, i.e., the diffusion of ideas within any given time window. As an example, one can say that during an epidemic disease outbreak in a small community, according to contact patterns driven from mobile phone data, the disease would only spread to one-third of individuals. Later, we apply randomized null models to demonstrate that boundaries of the reachability ratio in the temporal network of user behaviors are not accidental, and a small random change in the time of interaction between individuals - such as alternating the frequency of contacts or redistributing them - destroys the daily repeating patterns observed in their behaviors. Furthermore, these randomizations significantly affect some of the network metrics like average reachability ratio.

The redundant mapping from genotype to phenotype is common in evolutionary algorithms, where multiple genotypes can map to the same phenotype. Such a redundancy has been suggested to make an evolutionary system robust as well as evolvable. However, the impact of the redundant genotype-to-phenotype mapping and its resulted robustness and evolvability have not been well characterized quantitatively. In this article, we used a Boolean linear genetic programming system to construct a weighted and directed phenotype network, where vertices are phenotypes and a weighted link represents the number of possible point mutations that can transition genotypes from one phenotype to another. The direction of the links ensures moving from less fit phenotypes to fitter or equally fit ones. We used two fitness functions to investigate how it influences the network structure. Then we employed the Hyperlink-Induced Topic Search (HITS) algorithm to quantitatively characterize the evolvability and accessibility of phenotypes in the network. We found more robust phenotypes are both more evolvable and accessible. Our results help elucidate the effects of redundant mapping and the relationship of robustness, evolvability, and accessibility.

Stock market volatility is influenced by information release, dissemination, and public acceptance. With the increasing volume and speed of social media, the effects of Web information on stock markets are becoming increasingly salient. However, studies of the effects of Web media on stock markets lack both depth and breadth due to the challenges in automatically acquiring and analyzing massive amounts of relevant information. In this study, we systematically reviewed 229 research articles on quantifying the interplay between Web media and stock markets from the fields of Finance, Management Information Systems, and Computer Science. In particular, we first categorized the representative works in terms of media type and then summarized the core techniques for converting textual information into machine-friendly forms. Finally, we compared the analysis models used to capture the hidden relationships between Web media and stock movements. Our goal is to clarify current cutting-edge research and its possible future directions to fully understand the mechanisms of Web information percolation and its impact on stock markets from the perspectives of investors cognitive behaviors, corporate governance, and stock market regulation.

Network coding is an effective idea to boost the capacity of wireless networks, and a variety of studies have explored its advantages in different scenarios. However, there is not much analytical study on throughput and end-to-end delay of network coding in multi-hop wireless networks considering the specifications of IEEE 802.11 Distributed Coordination Function. In this paper, we utilize queuing theory to propose an analytical framework for bidirectional unicast flows in multi-hop wireless mesh networks. We study the throughput and end-to-end delay of inter-flow network coding under the IEEE 802.11 standard with CSMA/CA random access and exponential back-off time considering clock freezing and virtual carrier sensing, and formulate several parameters such as the probability of successful transmission in terms of bit error rate and collision probability, waiting time of packets at nodes, and retransmission mechanism. Our model uses a multi-class queuing network with stable queues, where coded packets have a nonpreemptive higher priority over native packets, and forwarding of native packets is not delayed if no coding opportunities are available. Finally, we use computer simulations to verify the accuracy of our analytical model.

In mobile computing, the location awareness of a mobile device and its user enables numerous personalized and social services such as recommendation of products and sharing current locations on social networks. Extending positioning services to indoor environments augments the value of the mobile communication market vastly. Due to serious signal attenuation, navigation satellites are incapable, and a common approach is to use or deploy small-scale radio frequency transmitters. When deploying these radio beacons, it is crucial to use a small number of them to provide high-quality positioning services. Such a deployment task is a challenging optimization problem, and the system administrators would benefit from having a spectrum of solutions with varying balance between cost and quality. In this study, we propose an Evolutionary Algorithm (EA) to tackle the problem. Using a cost-quality adjustment parameter, our EA framework is able to provide a set of solution options to meet varying requirements balancing cost and quality. This property is a result of the parallel population-based search of EAs, and can be very useful in real-world engineering applications.

In recent years, a significant amount of research has been conducted to explore the benefits of network coding in different scenarios, from both theoretical and simulation perspectives. In this paper, we utilize queueing theory to propose an analytical framework for bidirectional unicast flows in multi-hop wireless mesh networks, and study throughput of inter-flow network coding. We analytically determine performance metrics such as the probability of successful transmission in terms of collision probability, and feedback mechanism and retransmission. Regarding the coding process, our model uses a multi-class queueing network where coded packets are separated from native packets and have a non-preemptive higher priority over native packets, and both queues are in a stable state. Finally, we use simulations to verify the accuracy of our analytical model.

The opportunistic forwarding paradigm is an emerging technology that takes advantage of the broadcast nature of wireless communications to compensate the channel unreliability. In this paper, we propose an analytical model based on Markov Chain to evaluate the performance of opportunistic forwarding. In our model, the network state is described by the combination of the packet advancement progress and the schedule of opportunistic forwarding, it could provide better understanding of the multi-packet transmissions in a network. This paper contains two simulation studies based on the iterative estimation and the random walk to show the transmission cost of the batched opportunistic data forwarding.

Opportunistic data forwarding has proven to be a powerful technique to achieve a high throughput in wireless mesh networks. It proactively utilizes the link quality variation rather than fighting it. In this article, we propose a time-based coordination scheme of opportunistic forwarding, dubbed ExOR Compact, that uses network coding to provide a reliable data transfer service at the network layer. Computer simulation shows how the proposal stacks against the seminal work on opportunistic forwarding and traditional IP forwarding.

To study the influence of information on the behavior of stock markets, a common strategy in previous studies has been to concatenate the features of various information sources into one compound feature vector, a procedure that makes it more difficult to distinguish the effects of different information sources. We maintain that capturing the intrinsic relations among multiple information sources is important for predicting stock trends. The challenge lies in modeling the complex space of various sources and types of information and studying the effects of this information on stock market behavior. For this purpose, we introduce a tensor-based information framework to predict stock movements. Specifically, our framework models the complex investor information environment with tensors. A global dimensionality-reduction algorithm is used to capture the links among various information sources in a tensor, and a sequence of tensors is used to represent information gathered over time. Finally, a tensor-based predictive model to forecast stock movements, which is in essence a high-order tensor regression learning problem, is presented. Experiments performed on an entire year of data for China Securities Index stocks demonstrate that a trading system based on our framework outperforms the classic Top- N trading strategy and two state-of-the-art media-aware trading algorithms.

In recent years, network coding has emerged as an innovative method that helps wireless network approaches its maximum capacity, by combining multiple unicasts in one broadcast. However, the majority of research conducted in this area is yet to fully utilize the broadcasting nature of wireless networks, and still assumes fixed route between the source and destination that every packet should travel through. This assumption not only limits coding opportunities, but can also cause buffer overflow in some specific intermediate nodes. Although some studies considered scattering of the flows dynamically in the network, they still face some limitations. This paper explains pros and cons of some prominent research in network coding and proposes FlexONC (Flexible and Opportunistic Network Coding) as a solution to such issues. The performance results show that FlexONC outperforms previous methods especially in worse quality networks, by better utilizing redundant packets spread in the network.

Opportunistic forwarding and network coding utilize the broadcasting nature of wireless transmission and fluctuation of link quality for enhanced performance in multi-hop wireless networks. However, TCP is not well supported because of the way they operate. The frequent occurrences of dropped packets and out-of-order arrival of them in opportunistic forwarding and decoding delay in network coding overthrow TCP's congestion control. We propose a mechanism, dubbed TCPFender, for TCP to function over the network layer that uses opportunistic data forwarding and network coding, to properly conduct congestion control in TCP and provide reliable data transport. Our experiment shows that TCPFender achieves significantly higher throughput compared to TCP over IP in a simulated wireless mesh.

Delay-tolerant networking effectively extends the network connectivity in the time domain, and endows communications devices with enhanced data transfer capabilities. Network coding on the other hand enables us to approach the information capacity of networks by allowing intermediate nodes to process data en route. Both of these were major principal breakthroughs in mobile and wireless communications in the past decade or so. As reported in this article, we are interested in how network coding battles such challenged networks as DTN from an experimental perspective. We conducted tests with both real smart mobile devices and computer simulation and found conditions where their results match. This would give us confidence of using computer simulation to study larger delay-tolerant networks with and without network coding at a much manageable cost.

Indoor localization using mobile devices such as smartphones remains a challenging problem as GPS (Global Positioning System) does not work inside buildings and the accuracy of other localization techniques typically comes at the expense of additional infrastructure or cumbersome war-driving. For such environments, we propose a localization scheme which uses motion information from the smartphone's accelerometer, magnetometer, and gyroscope sensors to detect steps and estimate direction changes. At the same time, we use a Wi-Fi based fingerprinting technique for independent position estimation. These measurements along with an internal representation of the environment are combined using a Bayesian filter. This system will allow us to reduce the amount of training required and work in sparse Wi-Fi environments. We test our approach in two real-world environments to show the benefits of incorporating user motion for indoor localization.

BACKGROUND: Epistasis has been historically used to describe the phenomenon that the effect of a given gene on a phenotype can be dependent on one or more other genes, and is an essential element for understanding the association between genetic and phenotypic variations. Quantifying epistasis of orders higher than two is very challenging due to both the computational complexity of enumerating all possible combinations in genome-wide data and the lack of efficient and effective methodologies. OBJECTIVES: In this study, we propose a fast, non-parametric, and model-free measure for three-way epistasis. METHODS: Such a measure is based on information gain, and is able to separate all lower order effects from pure three-way epistasis. RESULTS: Our method was verified on synthetic data and applied to real data from a candidate-gene study of tuberculosis in a West African population. In the tuberculosis data, we found a statistically significant pure three-way epistatic interaction effect that was stronger than any lower-order associations. CONCLUSION: Our study provides a methodological basis for detecting and characterizing high-order gene-gene interactions in genetic association studies.

Opportunistic data forwarding draws more and more attention in the research community of wireless network after the initial work ExOR was published. However, as far as we know, all existing opportunistic data forwarding only use the nodes which are included in the forwarder list in the entire forwarding progress. In fact, even if a node is not a listed forwarder in the forwarder list, but it is on the direction from source node to destination node, and when it successfully overhears some packets by opportunity, the node actually can be utilized in the opportunistic data forwarding progress. In this paper, we propose the local cooperative relay for opportunistic data forwarding in mobile ad-hoc networks. In general, three contributions we have in this paper, 1) we open more node to participate in the opportunistic data forwarding even though the nodes are not included in the forwarder list, 2) we propose the procedure to select the best local relay node, namely the helper-node, from many candidates but require no inner communication between them, 3) the helper-node is selected just when it is needed, and the such real time selection can tolerate and bridge vulnerable links in mobile networks.

The link quality variation of wireless channels has been a challenging issue in data communications until recent explicit exploration in utilizing this characteristic. The same broadcast transmission may be perceived significantly differently, and usually independently, by receivers at different geographic locations. Furthermore, even the same stationary receiver may experience drastic link quality fluctuation over time. The combination of link-quality variation with the broadcasting nature of wireless channels has revealed a direction in the research of wireless networking, namely, cooperative communication. Research on cooperative communication started to attract interests in the community at the physical layer but more recently its importance and usability have also been realized at upper layers of the network protocol stack. In this article, we tackle the problem of opportunistic data transfer in mobile ad hoc networks. Our solution is called Cooperative Opportunistic Routing in Mobile Ad hoc Networks (CORMAN). It is a pure network layer scheme that can be built atop off-the-shelf wireless networking equipment. Nodes in the network use a lightweight proactive source routing protocol to determine a list of intermediate nodes that the data packets should follow en route to the destination. Here, when a data packet is broadcast by an upstream node and has happened to be received by a downstream node further along the route, it continues its way from there and thus will arrive at the destination node sooner. This is achieved through cooperative data communication at the link and network layers. This work is a powerful extension to the pioneering work of ExOR. We test CORMAN and compare it to AODV, and observe significant performance improvement in varying mobile settings.

Innovative routing in mobile ad hoc networks is crucial for unleashing the full potential of such networks. In this paper, we propose a new Proactive Source Routing (PSR) protocol that has a very small communication overhead but provides nodes with more network structure information than distance-vector based protocols. The value of the source routing protocol includes: 1) better control of path selection by the source nodes for congestion avoidance, load and energy consumption balancing, and bypassing untrusted areas, 2) alleviation of IP forwarding at intermediate nodes, and 3) support for opportunistic data forwarding. PSR complements DSR as a proactive counterpart to provide responsive data transportation services in heavily loaded networks. Our simulation results show that PSR achieves performance similar to OLSR and DSDV, but with only a small fraction of the communication overhead.

In recent years, "folksonomy''-like systems such as Wikipedia and Delicious Social Bookmarking have achieved huge success. User collaboration is the defining characteristic of such systems. For indoor positioning mechanisms, we argue that it is also possible to incorporate collaboration in order to improve system performance, especially for fingerprinting based approaches. In this paper, we propose a robust and efficient model for integrating human-centric collaborative feedback within a baseline Wi-Fi fingerprinting-based indoor positioning system. Experiments show that the baseline system performance (i.e., positioning error) is improved by collecting both positive and negative feedback from users. Moreover, the feedback model is robust with respect to malicious feedback, quickly self-correcting based on subsequent helpful feedback from users.

Self-publication of news on Web sites is becoming a common application platform to enable more engaging interaction among users. Discussion in the form of comments following news postings can be effectively facilitated if the service provider can recommend articles based on not only the original news itself but also the thread of changing comments. This turns the traditional news recommendation to a "discussion moderator" that can intelligently assist online forums. In this work, we present a framework to implement such adaptive news recommendation. In addition, to alleviate the problem of recommending essentially identical articles, the relationship (duplication, generalization or specialization) between suggested news articles and the original posting is investigated. Experiments indicate that our proposed solutions provide an enhanced news recommendation service in forum-based social media.

Reading and Commenting online news is becoming a common user behavior in social media. Discussion in the form of comments following news postings can be effectively facilitated if the service provider can recommend articles based on not only the original news itself but also the thread of changing comments. This turns the traditional news recommendation to a "discussion moderator" that can intelligently assist online forums. In this work, we present a framework to recommend relevant information in the forum-based social media using user comments. When incorporating user comments, we consider structural and semantic information carried by them. Experiments indicate that our proposed solutions provide an effective recommendation service.

Efficient and effective deployment of IEEE 802.16 networks to service an area of users with certain traffic demands is an important network planning problem. We resort to an evolutionary approach in order to yield good approximation solutions. In our method, novel genetic variation operations are proposed to incorporate the feature of this real-world application of evolutionary algorithm.

The Physical Layer of the IEEE 802.11 standard family provides a set of different modulation and coding schemes and, thus, a multitude of data rates. However, the Standard itself does not specify a mechanism to select adaptively among these data rates to improve the network throughput. An efficient rate adaptation scheme should aim to improve channel utilization through selection of optimal data rates that suits current channel conditions. This paper proposes a rate adaptation technique that: 1) exploits the RSS of received DATA frame to recommend higher data rate for subsequent transmissions, 2) distinguishes causes of a frame loss by recording RSS of a CTS/ACK frame to predict if the channel quality is deteriorating or not, and 3) utilizes a NAK-frame to diagnose frame losses due to channel fading. Our scheme, dubbed as Differential Rate Adaptation with NAKAssisted Loss Differentiation (DRANLD), is simulated using ns-2 and shown to adapt well to rapidly fluctuating channels.

Network coding has been used to increase transportation capabilities in wireless mesh networks. In mesh networks, the coding opportunities depend on the co-location of multiple traffic flows. With fixed routes given by a routing protocol, the coding opportunities are limited. This paper presents a new protocol called BEND, which combines the features of network coding and opportunistic forwarding in 802.11-based mesh networks to create more coding opportunities in the network. Taking advantage of redundancy of packets among the forwarder candidates, our protocol bends the routes locally and dynamically to attain better coding opportunities. This higher coding gain is verified using a network simulator.

Wireless mesh networks are flexible solutions to extend services from wireless LANs. The current IEEE 802.11 Specification, however, needs to be modified in various ways to be a suitable technology for this purpose. In particular, in order to handle the well-known hidden node problem (HNP), the Specification adopts MACAW by employing an RTS/CTS/DATA/ACK 4-way handshake. Some flaws of this scheme have been noticed, e.g. the Masked Node Problem (MNP). In this work, we identify a critical problem of the Specification's 4-way handshake, called persistent pseudo-clearance (PPC). PPC occurs when for two sender/receiver pairs a CTS from one pair's receiver collides with the DATA frames of the other pair. This logjam can persist for a period of time despite of the random backoff the senders employ. The persistent frame losses in PPC can cause more serious problems. The effect of giving up a frame transfer after reaching the maximum number of retries can propagate to upper layers, causing routing errors or TCP sender backoff. Multicast RTS (or MRTS) provides a good solution framework to break the cycle of losses and retransmissions between such peers. With minimal modification to MRTS, we provide an effective and efficient solution to PPC. Our experiments show that MRTS breaks the logjam of PPC while fully utilizing the network capacity.

A network of sensors can be used to obtain state-based data from the area in which they are deployed. To reduce costs, the data, sent via intermediate sensors to a sink, is often aggregated (or compressed). This compression is done by a subset of the sensors called aggregators. Since sensors are usually equipped with small and unreplenishable energy reserves, a critical issue is to strategically deploy an appropriate number of aggregators so as to minimize the amount of energy consumed by transporting and aggregating the data. In this paper, we first study single-level aggregation and propose an Energy-Efficient Protocol for Aggregator Selection (EPAS). Then, we generalize it to an aggregation hierarchy and extend EPAS to a Hierarchical Energy-Efficient Protocol for Aggregator Selection (hEPAS). We derive the optimal number of aggregators with generalized compression and power-consumption models, and present fully distributed algorithms for aggregator deployment. Simulation results show that our algorithms significantly reduce the energy consumption for data collection in wireless sensor networks. Moreover, the algorithms do not rely on particular routing protocols, and are thus applicable to a broad spectrum of application environments.

We present a series of approximation algorithms for finding a small weakly-connected dominating set (WCDS) in a given graph to be used in clustering mobile ad hoc networks. The structure of a graph can be simplified using WCDS's and made more succinct for routing in ad hoc networks. The theoretical performance ratio of these algorithms is O(ln Δ) compared to the minimum size WCDS, where Δ is the maximum degree of the input graph. The first two algorithms are based on the centralized approximation algorithms of Guha and Khuller cite guha-khuller-1998 for finding small connected dominating sets (CDS's). The main contribution of this work is a completely distributed algorithm for finding small WCDS's and the performance of this algorithm is shown to be very close to that of the centralized approach. Comparisons between our work and some previous work (CDS-based) are also given in terms of the size of resultant dominating sets and graph connectivity degradation.