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1. CoNICE: Consensus in Intermittently-Connected Environments by Exploiting Naming with Application to Emergency Response

   https://doi.org/10.1109/ICNP49622.2020.9259370  

   Jahanian, Mohammad; Ramakrishnan, K. K. (October 2020, 2020 IEEE 28th International Conference on Network Protocols (ICNP))

   null (Ed.)

   In many scenarios, information must be disseminated over intermittently-connected environments when network infrastructure becomes unavailable. Example scenarios include disasters in which first responders need to send updates about their critical tasks. If such updates pertain to a shared data set (e.g., pins on a map), their consistent dissemination is important. We can achieve this through causal ordering and consensus. Popular consensus algorithms, such as Paxos and Raft, are most suited for connected environments with reliable links. While some work has been done on designing consensus algorithms for intermittently-connected environments, such as the One-Third Rule (OTR) algorithm, there is need to improve their efficiency and timely completion. We propose CoNICE, a framework to ensure consistent dissemination of updates among users in intermittently-connected, infrastructure-less environments. It achieves efficiency by exploiting hierarchical namespaces for faster convergence, and lower communication overhead. CoNICE provides three levels of consistency to users' views, namely replication, causality and agreement. It uses epidemic propagation to provide adequate replication ratios, and optimizes and extends Vector Clocks to provide causality. To ensure agreement, CoNICE extends basic OTR to support long-term fragmentation and critical decision invalidation scenarios. We integrate the multilevel consistency schema of CoNICE, with a naming schema that follows a topic hierarchy-based dissemination framework, to improve functionality and performance. Performing city-scale simulation experiments, we demonstrate that CoNICE is effective in achieving its consistency goals, and is efficient and scalable in the time for convergence and utilized network resources. 

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2. A randomized incremental primal-dual method for decentralized consensus optimization

   https://doi.org/10.1142/S0219530519410082  

   Chen, Chenxi; Chen, Yunmei; Ye, Xiaojing (November 2019, Analysis and Applications)

   We consider a class of convex decentralized consensus optimization problems over connected multi-agent networks. Each agent in the network holds its local objective function privately, and can only communicate with its directly connected agents during the computation to find the minimizer of the sum of all objective functions. We propose a randomized incremental primal-dual method to solve this problem, where the dual variable over the network in each iteration is only updated at a randomly selected node, whereas the dual variables elsewhere remain the same as in the previous iteration. Thus, the communication only occurs in the neighborhood of the selected node in each iteration and hence can greatly reduce the chance of communication delay and failure in the standard fully synchronized consensus algorithms. We provide comprehensive convergence analysis including convergence rates of the primal residual and consensus error of the proposed algorithm, and conduct numerical experiments to show its performance using both uniform sampling and important sampling as node selection strategy. 

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3. Helix: A RAN Slicing Based Scheduling Framework for Massive MIMO Networks

   An, Qing; Pandey, Divyanshu; Doost-Mohammady, Rahman; Sabharwal, Ashutosh; Shakkottai, Arinivas (July 2024, arXivorg)

   An important aspect of 5G networks is the development of Radio Access Network (RAN) slicing, a concept wherein the virtualized infrastructure of wireless networks is subdivided into slices (or enterprises), tailored to fulfill specific use-cases. A key focus in this context is the efficient radio resource allocation to meet various enterprises’ service-level agreements (SLAs). In this work, we introduce Helix: a channel-aware and SLAaware RAN slicing framework for massive multiple input multiple output (MIMO) networks where resource allocation extends to incorporate the spatial dimension available through beamforming. Essentially, the same time-frequency resource block (RB) can be shared across multiple users through multiple antennas. Notably, certain enterprises, particularly those operating critical infrastructure, necessitate dedicated RB allocation, denoted as private networks, to ensure security. Conversely, some enterprises would allow resource sharing with others in the public network to maintain network performance while minimizing capital expenditure. Building upon this understanding, Helix comprises scheduling schemes under both scenarios: where different slices share the same set of RBs, and where they require exclusivity of allocated RBs. We validate the efficacy of our proposed schedulers through simulation by utilizing a channel data set collected from a real-world massive MIMO testbed. Our assessments demonstrate that resource sharing across slices using our approach can lead up to 60.9% reduction in RB usage compared to other approaches. Moreover, our proposed schedulers exhibit significantly enhanced operational efficiency, with significantly faster running time compared to exhaustive greedy approaches while meeting the stringent 5G sub-millisecond-level latency requirement. 

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4. Helix: A RAN Slicing Based Scheduling Framework for Massive MIMO Networks

   https://doi.org/10.1145/3696399  

   An, Qing; Pandey, Divyanshu; Doost-Mohammady, Rahman; Sabharwal, Ashutosh; Shakkottai, Srinivas (December 2024, Proceedings of the ACM on Networking)

   An important aspect of 5G networks is the development of Radio Access Network (RAN) slicing, a concept wherein the virtualized infrastructure of wireless networks is subdivided into slices (or enterprises), tailored to fulfill specific use-cases. A key focus in this context is the efficient radio resource allocation to meet various enterprises' service-level agreements (SLAs). In this work, we introduce Helix: a channel-aware and SLA-aware RAN slicing framework for massive multiple input multiple output (MIMO) networks where resource allocation extends to incorporate the spatial dimension available through beamforming. Essentially, the same time-frequency resource block (RB) can be shared across multiple users through multiple antennas. Notably, certain enterprises, particularly those operating critical infrastructure, necessitate dedicated RB allocation, denoted as private networks, to ensure security. Conversely, some enterprises would allow resource sharing with others in the public network to maintain network performance while minimizing capital expenditure. Building upon this understanding, Helix comprises scheduling schemes under both scenarios: where different slices share the same set of RBs, and where they require exclusivity of allocated RBs. We validate the efficacy of our proposed schedulers through simulation by utilizing a channel data set collected from a real-world massive MIMO testbed. Our assessments demonstrate that resource sharing across slices using our approach can lead up to 60.9% reduction in RB usage compared to other approaches. Moreover, our proposed schedulers exhibit significantly enhanced operational efficiency, with significantly faster running time compared to exhaustive greedy approaches while meeting the stringent 5G sub-millisecond-level latency requirement. 

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5. Modeling and Defense of Social Virtual Reality Attacks Inducing Cybersickness

   https://doi.org/10.1109/TDSC.2021.3121216  

   Valluripally, Samaikya; Gulhane, Aniket; Hoque, Khaza Anuarul; Calyam, Prasad (October 2021, IEEE Transactions on Dependable and Secure Computing)

   Social Virtual Reality Learning Environments (VRLE) offer a new medium for flexible and immersive learning environments with geo-distributed users. Ensuring user safety in VRLE application domains such as education, flight simulations, military training is of utmost importance. Specifically, there is a need to study the impact of ‘`immersion attacks’' (e.g., chaperone attack, occlusion) and other types of attacks/faults (e.g., unauthorized access, network congestion) that may cause user safety issues (i.e., inducing of cybersickness). In this paper, we present a novel framework to quantify the security, privacy issues triggered via immersion attacks and other types of attacks/faults. By using a real-world social VRLE viz., vSocial and creating a novel attack-fault tree model, we show that such attacks can induce undesirable levels of cybersickness. Next, we convert these attack-fault trees into stochastic timed automata (STA) representations to perform statistical model checking for a given attacker profile. Using this model checking approach, we determine the most vulnerable threat scenarios that can trigger high occurrence cases of cybersickness for VRLE users. Lastly, we show the effectiveness of our attack-fault tree modeling by incorporating suitable design principles such as hardening, diversity, redundancy and principle of least privilege to ensure user safety in a VRLE session. 

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