The deployment of commercial-off-the-shelf (COTS) GPUs in space has emerged as a promising approach for supporting inorbit deep neural network (DNN) inference. However, unlike terrestrial environments, understanding the impact of space radiation on COTS GPU-enabled DNNs is critical. This is challenging because existing methods, such as real-world radiation testing and software emulation, fail to link radiation-induced memory errors to runtime DNN behaviors. In this paper, we propose REMU, a memory-aware Radiation EMUlator to fill this gap. REMU introduces a dual addressing mechanism across virtual, physical, and DRAM memory spaces, enabling precise mapping and efficient injection of radiation-induced errors from DRAM to runtime DNN inference. Extensive evaluations across 10 well-known DNN models and 2 typical in-orbit computing tasks demonstrate the effectiveness of REMU, providing valuable insights for understanding the resilience of runtime DNN inferences on space radiations.

Low-earth-orbit (LEO) satellite mega-constellations enable universal Internet access from space. To ensure mission safety, LEO satellites conduct orbital maneuvers to prevent physical collisions with other space objects. These maneuvers challenge the performance of predictive TCP congestion control, which mitigates unnecessary throughput reductions caused by handover-induced data loss/delay. Our empirical study shows that these maneuvers can downgrade predictive TCP congestion control’s throughput by 17.63% due to the maneuvering satellite’s cumulative position deviations. The root cause is that end users rely on coarse-grained Two-Line Elements (TLEs) to predict satellites’ orbits, thus suffering from inaccurate predictions. Although these negative impacts seem unsolvable, fine-grained ephemeris can help end users enhance the accuracy of predicting handover events and effectively manage the durations of freezing congestion windows to a more affordable level. We then propose MATCP, a Maneuver-Aware TCP congestion control scheme to enhance the performance of TCP transport in maneuverable satellite networks. Compared to existing predictive congestion control, MATCP leverages ephemeris to achieve more accurate predictions of satellite handovers, and freezes the congestion window at predicted handover timing with a shorter freezing duration, thus improving the throughput of predictive congestion control. Our evaluations, driven by Starlink’s space situational awareness (SSA) dataset, validate that MATCP can safely prevent 94.21% of maneuver-induced throughput reductions in predictive TCP congestion control and increase throughput by 3.83× compared to TCP CUBIC and 20.07% compared to existing predictive satellite congestion control (SATCP).

Direct-to-cell satellites enable global network services for our regular phones/IoTs via 4G, 5G, and beyond. To enforce highly available, trustworthy, and roaming policy-compliant network services, they heavily rely on user geolocation and timing information from external global navigation satellite systems (GNSS) to assist with their radio access, authentication, and authorization. Our analysis and field tests reveal that, this cross-technology over-reliance propagates satellite navigation's defects to direct-to-cell satellite networks, leading to diverse issues such as intermittent connectivity, over/under-billing, unauthorized services, and service denials even when direct-to-cell satellites are accessible. Our solution, SN2, adopts the "fate-sharing" principle to reuse direct-to-cell satellites themselves for self-navigating networks. By exploiting the flexible tradeoffs between satellite network availability and navigation accuracy, it enables "good enough" built-in navigation for highly available and functionally correct network services at a negligible cost of hardware or communication resources. Our evaluations with commodity satellite phones and 3GPP NTN protocol stacks demonstrate SN2's 4.4–23.5× network availability boost and 1.9–12.3× access latency reduction over legacy solutions.

The recent renaissance of low Earth orbit (LEO) satellite networks expands the boundaries of global Internet access, but also introduces substantial new challenges for existing end-to-end congestion control algorithms (CCAs). The rapid and continuous movement of LEO satellites leads to infrastructure-level dynamics, resulting in frequent, LEO-dynamics-induced changes in link capacity, delay, and packet loss rate, which can further mislead the rate control in existing CCAs and cause self-limited performance.

Do we really need 10,000s of Low Earth Orbit (LEO) satellites to meet huge global Internet demands? While proven feasible and valuable, such LEO mega-constellation networks have raised concerns about their prohibitive capital expenditures, market monopoly, and unsustainable use of space. Instead, our analysis reveals that most of their satellites can be wasted due to their mismatch with physically uneven demands. We thus propose TinyLEO, a software-defined solution to shrink LEO network size for enormous global demands via dynamic spatiotemporal supply-demand matching. TinyLEO sparsifies satellite supplies on demand by combining diverse yet sparse orbits, hides complexities of this sparse LEO network via orbital model predictive control, and shifts the responsibility for handling these complexities to its geographic segment anycast for higher network usability, lower resource wastes, faster failovers, simpler satellites, and more flexible network orchestration. We have prototyped TinyLEO as a community toolkit for open research. Our evaluation using this toolkit shows that TinyLEO can compress the existing LEO mega-constellation network size by 2.0–7.9×, cut control plane costs by 1–3 orders of magnitude, and maintain the same demands and comparable data plane performance.

Leveraging direct-to-cell (DTC) satellites in low-earth orbits (LEO) to directly provide communication services for terrestrial cellphones is gaining popularity in recent years. However, the unique characteristics of the wireless medium in space-ground communication, combined with the dynamic behavior of LEO satellites, raise a new privacy leakage risk that an adversary eavesdropping on DTC broadcasts could steal the physical locations of active users. In this paper, we investigate new techniques to analyze the location leakage risks in emerging LEO direct-to-cell satellite networks (DCSN). We present DCATOR1DCATOR indicates the abbreviation of DCSN terminal locator. , a novel location leakage analyzer which continuously monitors DTC signaling messages in broadcast channels, extracts various location clues and combines them with the time-varying satellite trajectories to infer the physical locations of active users. We use DCATOR to analyze the consequences if an adversary is able to continuously monitor and process broadcast DTC signaling to deduce the locations of other users within the same satellite coverage area, in three representative DCSNs: (i) the operational Iridium; (ii) the developing Starlink DTC; and (iii) a DCSN based on the latest 3GPP NTN standards. Our extensive experiments demonstrate the existence of location leakages in real DCSNs, and in the worst case an adversary can precisely track the locations of other users within hundreds of meters. Finally, we propose privacy-enhancing countermeasures for DCSNs.

In this paper, we perform a systematic study to explore a pivotal problem facing the web community: is current distributed web cache ready for future satellite Internet? First, through a worldwide performance measurement based on the RIPE Atlas platform and Starlink, the largest low-earth orbit (LEO) satellite network (LSN) today, we identify that the uneven deployment of current distributed cache servers, inter-ISP meandering routes and the last-mile congestion on LEO links jointly prevent existing terrestrial web cache from providing low-latency web access for users in emerging LSNs. Second, we propose Spache, a novel web caching system which addresses the limitations of existing ground-only cache by exploiting a bold idea: integrating web cache into LEO satellites to achieve ubiquitous and low-latency web services. Specifically, Spache leverages a key feature of LSNs called communication schedule to efficiently prefetch web contents on satellites, and adopts a schedule-driven partitioning strategy to avoid cache pollution involved by LEO mobility. Finally, we implement a prototype of Spache, and evaluate it based on real-world HTTP traces and data-driven LSN simulation. Extensive evaluations demonstrate that as compared to existing distributed caching solutions, Spache can improve cache hit ratio by 19.8% on average, reduce latency by up to 17.7%, and maintains consistently low web browsing latency for global LSN users.

User-perceived latency is important for the quality of experience (QoE) of wide-area real-time communications (RTC). With the rapid development of low Earth orbit (LEO) mega-constellations, this paper explores a futuristic yet important problem facing the RTC community:can we exploit emerging mega-constellations to facilitate low-latency RTC globally?We carry out our quest in three steps. First, through a measurement study associated with a large number of geo-distributed RTC users, we quantitatively expose that themeandering routesin theclient-to-cloudandinter-cloud-sitesegment of existing cloud-based RTC architecture are critical culprits for the high latency issue suffered by wide-area RTC sessions. Second, we proposeSpaceRTC, a satellite-cloud cooperative framework that dynamically selectsrelay serversupon satellites and cloud sites to build an overlay network which enables diverse close-to-optimal paths.SpaceRTCjudiciously allocates RTC flows of different sessions upon the network to facilitate low-latency interactions and adaptively selects bitrates to offer high user-perceived QoE in energy-limited space circumstance. Finally, we implement a testbed based on public constellation information and real-world RTC traces. Extensive experiments demonstrate thatSpaceRTCcan deliver near-optimal interactive latency, with up to 53.3% average latency reduction and 103.6% average bitrate improvement as compared to other state-of-the-art cloud-based solutions.

Low Earth Orbit (LEO) satellite network is undergoing an explosive expansion to enable high-speed Internet for numerous users anywhere on Earth. However, as a cyber-physical network, the LEO network's sustainable expansion is constrained by its harsh, crowded, and imbalanced physical environment. This position paper dives into two physical constraints for the LEO network scalability: the scale-out limit by satellite safety distances in crowded outer space, and the scale-up limit by the mismatch between the uniform LEO network capacity supply and geographically non-uniform global distribution of user demands. Traditional networking research pays less attention to these physical scaling limits, which may imply a call for a cyber-physical co-design to help the LEO network grow in the challenged space environment.

End-to-end congestion control algorithms (CCAs) are expected to perform well in any Internet path, including those paths with low-earth orbit (LEO) satellite links. In this paper, we conduct a performance study on various CCAs in an operational LEO satellite network. We find that existing CCAs struggle to deal with the drastic network variations caused by the mobility of LEO satellites, resulting in poor link utilization or high latency. Further, through an in-depth analysis, we identify the fundamental challenge is that existing end-to-end CCAs detect network congestion based on performance changes observed on the sender, but the unique LEO mobility can involve massive non-congestion performance changes which seriously mislead CCA behaviors. Finally, we explore and discuss possible solutions to mitigate the misleading effects of LEO mobility.

Emerging satellite Internet constellations such as SpaceX's Starlink will deploy thousands of broadband satellites and construct Low-Earth Orbit (LEO) satellite networks (LSNs) in space, significantly expanding the boundaries of today's terrestrial Internet. However, due to the unique global LEO dynamics, satellite routers will inevitably pass through uncontrolled areas, suffering from security threats. It should be important for satellite network operators (SNOs) to enable verifiable riskavoidance routing to identify path anomalies. In this paper, we present STARVERI, a novel network path verification framework tailored for emerging LSNs. STARVERI addresses the limitations of existing crypto-based and delay-based verification approaches and accomplishes efficient and accurate path verification by: (i) adopting a dynamic relay selection mechanism deployed in SNO's operation center to judiciously select verifiable relays for each communication pair over LSNs; and (ii) incorporating a lightweight path verification algorithm to dynamically verify each segment path split by distributed relays. We build an LSN simulator based on real constellation information and the results demonstrate that STARVERI can significantly improve the path verification accuracy and achieve lower router overhead compared with existing approaches.

Recently we have witnessed the active deployment of mega-constellations with hundreds to thousands of low-earth orbit (LEO) satellites, targeting at constructing LEO satellite networks (LSN) to provide ubiquitous Internet services globally. However, while the massive deployment of LEO satellites can improve the network survivability and performance of an LSN, it also involves additional sustainable challenges such as higher deployment cost, risk of satellite conjunction and space debris.In this paper, we investigate an important research problem facing the upcoming satellite Internet: from a network perspective, how many satellites exactly do we need to construct a survivable and performant LSN? To answer this question, we first formulate the survivable and performant LSN design (SPLD) problem, which aims to find the minimum number of needed satellites to construct an LSN that can provide sufficient amount of redundant paths, required link capacity and acceptable latency for traffic carried by the LSN. Second, to efficiently solve the tricky SPLD problem, we propose MegaReduce, a requirement-driven constellation optimization mechanism, which can calculate feasible solutions for SPLD in polynomial time. Finally, we conduct extensive trace-driven simulations to verify MegaReduce’s cost-effectiveness in constructing survivable and performant LSNs on demand, and showcase how MegaReduce can help optimize the incremental deployment and long-term maintenance of future satellite Internet.

Emerging integrated space and terrestrial networks (ISTN) built upon low earth orbit (LEO) satellite constellations aim at providing planet-wide Internet services, not only for residential users, but also for mobile users (e.g., in airplane and cruise scenarios). Efficiently managing global mobility and keeping connections active for mobile users is critical for ISTN operators. However, our quantitative analysis identifies that existing mobility management (MM) schemes suffer from frequent connection interruptions and long latency in ISTN scenarios. The fundamental challenge stems from a unique characteristic of ISTNs: not only users are mobile, but also core network infrastructures (i.e., LEO satellites) are frequently changing their locations in the network.To facilitate seamless and low-latency satellite Internet services, this paper presents SkyCastle, a novel network-based global mobility management mechanism. SkyCastle incorporates two key techniques to address frequent connection interruptions in ISTNs. First, to reduce the interruption time, SkyCastle adopts distributed satellite anchors to track the location changes of mobile nodes, manage handovers and avoid routing convergence. Second, SkyCastle leverages an anchor manager to schedule MM functionalities at satellites to reduce deployment costs while guaranteeing low latency. Extensive evaluations combining real constellation information and mobile user trajectories show that: SkyCastle can improve up to 55.8% uninterrupted time and reduce 47.8% latency as compared to other existing MM solutions.

With the rapid evolution of space-borne capabilities, space edge computing (SEC) is becoming a new computation paradigm for future integrated space and terrestrial networks. Satellite edges adopt advanced on-board hardware, which not only enables new opportunities to perform complex intelligent tasks in orbit, but also involves new challenges due to the additional energy consumption in power-constrained space environment.In this paper, we present Phoenix, an energy-efficient task scheduling framework for emerging SEC networks. Phoenix exploits a key insight that in the SEC network, there always exist a number of sunlit edges which are illuminated during the entire orbital period and have sufficient energy supplement from the sun. Phoenix accomplishes energy-efficient in-orbit computing by judiciously offloading space tasks to "sunlight-sufficient" edges or to the ground. Specifically, Phoenix first formulates the SEC battery energy optimizing (SBEO) problem which aims at minimizing the average battery energy consumption while satisfying various task completion constraints. Then Phoenix incorporates a sunlight-aware scheduling mechanism to solve the SBEO problem and schedule SEC tasks efficiently. Finally, we implement a Phoenix prototype and build an SEC testbed. Extensive data-driven evaluations demonstrate that as compared to other state-of-the-art solutions, Phoenix can effectively reduce up to 54.8% SEC battery energy consumption and prolong battery lifetime to 2.9× while still completing tasks on time.

Low Earth Orbit (LEO) satellite mega-constellations promise ubiquitous network services to "unconnected" users. But their upcoming global routing for Earth will be unstable due to exhaustive topology updates between satellites and Earth, inside an orbital shell, and across heterogeneous orbital shells. In real LEO networks, these multi-dimensional dynamics are interleaved and complicated by chaotic orbital maneuvers and random failures. They are less predictable than most satellite routing proposals expect and threaten these proposals' availability, efficiency, or resiliency at scale.

The emergent direct-to-cell Low-Earth Orbit (LEO) satellite mega-constellations promise ubiquitous LTE/5G access for our commodity phones and IoTs without terrestrial base stations. While their extreme scale and mobility help tolerate diverse attacks, we show that both new features are exploitable to amplify signaling protocol vulnerabilities inherited from LTE/5G and obfuscate attacks to threaten satellite services. We showcase this with SatOver, a control-plane cross-layer attack that lets a greedy terrestrial operator or a man-in-the-middle attacker block all direct-to-cell satellites in urban areas. SatOver can reuse terrestrial LTE/5G base stations or deploy commodity software-defined radios as false satellites, stealthily hijack victim devices, delay their satellite access, stop them from probing other satellites, and block the entire mega-constellation. Our real-world satellite tests, lab tests with commodity 3GPP NR/IoT-NTN stacks, and operational trace-driven emulation validate SatOver’s viability for attacking COTS and upcoming NTN phones/IoTs. We discuss potential defenses against SatOver’s attack amplification/obfuscation.

The integration of Mobile Edge Computing (MEC) and Low Earth Orbit (LEO) satellite networks holds the potential to offer ubiquitous computing services for ground users and has garnered significant attention recently. While there has been extensive research conducted in the field of Satellite Mobile Edge Computing (SMEC), research on edge server deployment in mega-constellation is overlooked. Edge servers require an appropriate quantification and placement before the implementation of computation offloading. The improper deployment strategy can result in high access latency and imbalance workload. In this paper, we propose a two-stage approach, called cluster-based small-scale server deloyment (CSSD), for small-scale placing and dynamic allocating edge servers that enable low access latency and workload balancing. Specifically, the offline stage is employed to determine the optimal placement of edge servers and the initial offloading mapping from access satellites to service satellites. The online stage, building dynamically adjusts the offloading mapping based on the spatiotemporal positions and workload of the satellites to balance system workload. Evaluation results show that CSSD outperforms other approaches with up to 16.37% and 35.38% enhancements in terms of workload standard deviation and user service rate while maintaining low access latency.

Delay-sensitive Web services are crucial applications in emerging low-earth orbit (LEO) satellite networks (LSNs). However, our real-world measurement study based on SpaceX's Starlink, the most widely used commercial LSN today, reveals that the endless and bursty packet losses over unstable LEO satellite links impose significant challenges on guaranteeing the quality of experience (QoE) of Web applications. We propose SatGuard, a distributed in-orbit loss recovery mechanism that can reduce user-perceived delay by completely concealing packet losses in the unstable and lossy LSN environment from endpoints. Specifically, SatGuard adopts a series of techniques to: (i) correctly migrate on-board packet buffer to support link-local retransmission under LEO dynamics; (ii) efficiently detect packet losses on satellite links; and (iii) ensure packet ordering for endpoints. We implement a SatGuard prototype, and conduct extensive trace-driven evaluations guided by public constellation information and real-world measurements. Our experiments demonstrate that, in comparison with other state-of-the-art approaches, SatGuard can significantly improve Web-based QoE, by reducing: (i) up to 48.3% of page load time for Web browsing; and (ii) up to 57.4% end-to-end communication delay for WebRTC.

We design and implementLDRP, a device-based, standard-compliant solution to latency diagnosis and reduction in mobile networks without root privilege.LDRPtakes a data-driven approach and works with a variety of latency-sensitive applications. After identifying elements in LTE uplink latency, we designLDRPthat can infer the critical parameter used in data transmission and infer them for diagnosis. In addition,LDRPdesignates small dummy messages, which precede uplink data transmissions, thus eliminating latency elements due to power-saving, scheduling, etc. It imposes proper timing control among dummy messages and data packets to handle various conflicts. We achieve the latency diagnosis and reduction without requiring root privilege and ensure the latency is no worse than the legacy LTE design. The design ofLDRPis also applicable for 5G. The evaluation shows that,LDRPinfers the latency with at most 4% error and reduces the median LTE uplink latency by a factor up to 7.4× (from 42 to 5 ms) for four apps over 4 mobile carriers.

Deep neural networks (DNNs) are widely deployed on real-world devices. Concerns regarding their security have gained great attention from researchers. Recently, a new weight modification attack called bit flip attack (BFA) was proposed, which exploits memory fault inject techniques such as row hammer to attack quantized models in the deployment stage. With only a few bit flips, the target model can be rendered useless as a random guesser or even be implanted with malicious functionalities. In this work, we seek to further reduce the number of bit flips. We propose a training-assisted bit flip attack, in which the adversary is involved in the training stage to build a high-risk model to release. This high-risk model, obtained coupled with a corresponding malicious model, behaves normally and can escape various detection methods. The results on benchmark datasets show that an adversary can easily convert this high-risk but normal model to a malicious one on victim’s side by flipping only one critical bit on average in the deployment stage. Moreover, our attack still poses a significant threat even when defenses are employed. The codes for reproducing main experiments are available at https://github.com/jianshuod/TBA.

Exploiting a constellation of small satellites to realize continuous earth observations (EO) is gaining popularity. Large-volume EO data acquired from space needs to be transferred to the ground. However, existing EO delivery approaches are either: (a) efficiency-limited, suffering from long delivery completion time due to the intermittent ground-space communication, or (b) scalability-limited since they fail to support concurrent delivery for multiple satellites in an EO constellation.To make big data delivery for emerging EO constellations fast and scalable, we propose Falcon, a multi-path EO delivery framework that wisely exploits diverse paths in broadband constellations to collaboratively deliver EO data effectively. In particular, we formulate the constellation-wide EO data multi-path download (CEOMD) problem, which aims at minimizing the delivery completion time of requested data for all EO sources. We prove the hardness of solving CEOMD, and further present a heuristic multipath routing and bandwidth allocation mechanism to tackle the technical challenges caused by time-varying satellite dynamics and flow contention, and solve the CEOMD problem efficiently. Evaluation results based on public orbital data of real EO constellations show that as compared to other state-of-the-art approaches, Falcon can reduce at least 51% delivery completion time for various data requests in large EO constellations.

Entering the “NewSpace” era, satellite Internet constellation (SIC) is expanding rapidly. However, while operating a large number of broadband satellites in free space enables great opportunities for ubiquitous and low-latency Internet services, it also involves new threats that were previously ignored to the energy-limited satellite systems. In this paper, we investigate the feasibility and impact of a new class of real risk in emerging SICs: energy drain attack. To this end, we play the role of an attacker and propose Starmelt,an energy drain attack mechanism that persistently injects malicious traffic that goes through the victim satellite from various geo-distributed locations, preventing the victim from hibernating and overusing the satellite battery to cut its lifetime. Further, we design a series of techniques to: (i) handle the path ambiguity challenge caused by various access selection and routing schemes to guarantee that the deliberately crafted traffic can precisely pass through the victim; (ii) leverage the tail energy consumption characteristics to reduce the detectability as well as the traffic cost of an attack. Extensive simulations based on real constellation knowledge demonstrate that Starmeltcan substantially increase on-board energy consumption, reducing up to 76% lifetime of the victim satellite under various power models and traffic patterns.

For its stringent QoE requirement, augmented reality (AR) has been widely hailed as a representative of ultra-high bandwidth and ultra-low latency apps that will be enabled by 5G networks/edge clouds. Such a portrait of AR by the telco and cloud industry raises an important research question - can 5G enable latency-critical applications such as (edge-assisted) AR? In this paper, we conduct to our knowledge the first in-depth measurement study of whether 5G mmWave in combination with in-network edge cloud can support the baseline edge-assisted object detection. After we discover 5G mmWave is unlikely to achieve the level of uplink network performance needed to support a baseline edge-assisted object detection implementation in the near future, we quantify the performance benefits in retrofitting app-level optimizations developed in the pre-5G era on top of baseline edge-assisted object detection, as well as the performance benefits from hardware upgrade on the edge. We find that these optimizations can significantly boost object detection performance over both LTE and 5G mmWave; however, the improvement with 5G mmWave over LTE is marginal, and 5G mmWave still fails to provide satisfactory performance in all scenarios under consideration. Overall, we conclude that today's 5G mmWave deployment is not a deciding factor in enabling edge-assisted object detection.

Low-earth-orbit (LEO) satellite mega-constellations, such as SpaceX Starlink, are under rocket-fast deployments and promise broadband Internet to remote areas that terrestrial networks cannot reach. For mission safety and sustainable uses of space, Starlink has adopted a proprietary onboard autonomous driving system for its extremely mobile LEO satellites. This paper demystifies and diagnoses its impacts on the LEO mega-constellation and satellite networks. We design a domain-specific method to characterize key components in Starlink's autonomous driving from various public space situational awareness datasets, including continuous orbit maintenance, collision avoidance, and maneuvers between orbital shells. Our analysis shows that, these operations have mixed impacts on the stability and performance of the entire mega-constellation, inter-satellite links, topology, and upper-layer network functions. To this end, we investigate and empirically assess the potential of networking-autonomous driving co-designs for the upcoming satellite networks.

To realize the Interconnection of Everything (IoE) in the 6G vision, the space-based, air-based, and ground-based networks have shown a trend of integration. Compared with the traditional communications system, Space-Air-Ground Integrated Networks (SAGINs) can provide a seamless global network connection, while making full use of different network characteristics for synergy and complementarity. However, the increasing global coverage of the Internet, the growing number and variety of smart terminals, and the emergence of various high-bandwidth services have led to an explosion in communication data transmission. Despite the continuous development of communication technologies such as airborne processing and forwarding and high-throughput satellites, the quality of service (QoS) and quality of experience (QoE) for different users still cannot be guaranteed due to the power limitations of satellites and the scarcity of spectrum resources. In this work, drawing on wireless edge caching, considering that the relay of SAGIN has edge caching capability, the hot task is cached in the network nodes in advance. More, this process is optimized using distributed Deep Reinforcement Learning (DRL), thereby reducing transmission delay and relieving the pressure of task offloading on space-based networks. Compared with advanced related works, the long-term node utilization, link utilization, long-term average revenue-to-cost ratio and acceptance ratio of the proposed algorithm are increased by about 4.22%, 31.36%, 11.75% and 7.14%, respectively.

Extreme mobility becomes a norm rather than an exception with emergent high-speed rails, drones, industrial IoT, and many more. However, 4G/5G mobility management is not always reliable in extreme mobility, with non-negligible failures and policy conflicts. The root cause is that, existing mobility management is primarily based on wireless signal strength. While reasonable in static and low mobility, it is vulnerable to dramatic wireless dynamics from extreme mobility in triggering, decision, and execution. We deviseREM, Reliable Extreme Mobility management for beyond 5G cellular networks while maintaining backward compatibility to 4G/5G.REMshifts to movement-based mobility management in the delay-Doppler domain. Its signaling overlay relaxes feedback via cross-band estimation, simplifies policies with provable conflict freedom, and stabilizes signaling via scheduling-based OTFS modulation. Our evaluation with operational high-speed rail datasets shows that,REMreduces failures comparable to static and low mobility, with low signaling and latency cost.REMreduces the network failures by up to an order of magnitude, eliminates policy conflicts, and improves application performance by 31.8% - 88.3% compared to legacy 4G/5G.

Emerging low earth orbit (LEO) satellites and geo-distributed ground station networks can assist pervasive and efficient Internet data delivery on a global scale. However, while promising, the improper integration of ingress satellite selection (ISS) and inter-satellite routing (ISR) can result in significantly high propagation latency and low network utilization. In this paper, we propose AeroPath, a ground-station-driven data delivery architecture that enables high-throughput data transmission while maintaining low latency. Specifically, to accomplish transmission efficiency, geo-distributed ground stations independently schedule flows over ground-satellite links in collaboration with ISR and cooperatively select inter-satellite paths to avoid bandwidth competition between different ground stations. Finally, we evaluate the effectiveness of AeroPath via extensive simulations driven by realistic constellation information. Evaluation results show that AeroPath can outperform other approaches with up to 24.1% and 18.5% improvement in terms of average system throughput and ground station utilization respectively under state-of-the-art constellation patterns.

The emergence of mega-constellations is the most promising satellite network trend in recent years, which brings new security challenges to the network layer and higher layers, such as DDoS, worm, and DNS pollution. Source address validation is one of the effective solutions in terrestrial networks, by filtering the invalid address and resisting the source address spoofing. Because of the time-vary topology in mega-constellations, the source address validation mechanism faces the severe problem of the anchor mobility, which leads to a sharp decline in SAVI (Source Address Validation Improvements) performance and increases the cost to maintain the user status. In this paper, we develop a source address spoofing avoidance mechanism under high movement (SASA) for mega-constellations. Specifically, we propose that the user and the satellite both maintain the user status. After the satellite signed the binding information by the private key, it forms the mapping between the authenticity of the user address and the initial access satellite on the user side. Moreover, when the handover occurs, the user safely transmits the authentication information to the new access satellite through asymmetric encryption to complete rebinding. Simulation results show that SASA can greatly reduce the rebinding cost of mega-constellations by 95.04% in Starlink and 81.84% in Kuiper.

The low-earth-orbit (LEO) satellite networks promise low-latency broadband network services to remote areas. LEO networks require deploying many satellites to serve numerous users in crowded terrestrial areas, thus forming a satellite mega-constellation. It is well known that this practice would lower network utilization, which may be exacerbated with recent LEO satellite mega-constellations. For LEO satellite mega-constellation networks with complex and dynamic structures, multiple functions, and diversified user demands, this paper presents a methodology for systematically analyzing utilization. Our study shows that operational mega-constellations today suffer from < 10% low network utilization. The root cause is twofold. First, today's evenly distributed satellites do not match the unevenly distributed terrestrial users, and the law of satellite movement keeps a satellite above sparsely populated areas most of the time. Second, a uniform LEO mega-constellation cannot simultaneously meet the heterogeneous demands from local satellite access (requiring more satellites to serve the enormous population in hotspots) and global satellite routing (requiring fewer satellites for shorter paths). Based on these findings, we analyze how to improve utilization from angles of constellation structure and network architecture design. We showcase it can increase the utilization with fewer and divergent satellites while retaining comparable performance to state-of-the-art.

Low-earth-orbit (LEO) satellite mega-constellations promise broadband, low-latency network infrastructure from space for terrestrial users in remote areas. However, they face new QoS bottlenecks from infrastructure mobility due to the fast-moving LEO satellites and earth’s rotations. Both cause frequent space-ground link churns and challenge the network latency, bandwidth, and availability at the global scale. Today’s LEO networks mask infrastructure mobility with fixed anchors (ground stations) but cause single-point bandwidth/latency bottlenecks. Instead, we design LBP to remove the LEO network’s QoS bottlenecks from infrastructure mobility. LBP removes remote terrestrial fixed anchors via geographic addressing for shorter latencies and more bandwidth. It adopts local, orbit direction-aware geographic routing to avoid global routing updates for high network availability. LBP further shortens the routing paths by refining handover policies by satellites’ orbital directions. Our experiments in controlled testbeds and trace-driven emulations validate LBP’s 1.64× network latency reduction, 9.66× more bandwidth, and improve network availability to 100%.

The wireless signal propagates via multipath arising from different reflections and penetration between a transmitter and receiver. Extracting multipath profiles (e.g., delay and Doppler along each path) from received signals enables many important applications, such as channel prediction and crossband channel estimation (i.e., estimating the channel on a different frequency). The benefit of multipath estimation further increases with mobility since the channel in that case is less stable and more important to track. Yet high-speed mobility poses significant challenges to multipath estimation. In this paper, instead of using time-frequency domain channel representation, we leverage the delay-Doppler domain representation to accurately extract and predict multipath properties. Specifically, we use impulses in the delay-Doppler domain as pilots to estimate the multipath parameters and apply the multipath information to predicting wireless channels as an example application. Our design rationale is that mobility is more predictable than the wireless channel since mobility has inertial while the wireless channel is the outcome of a complicated interaction between mobility, multipath, and noise. We evaluate our approach via both acoustic and RF experiments, including vehicular experiments using USRP. Our results show that the estimated multipath matches the ground truth, and the resulting channel prediction is more accurate than the traditional channel prediction schemes.

Is it worth and feasible to push mobile core network functions to low-earth-orbit (LEO) satellite mega-constellations? While this paradigm is being tested in space and promises new values, it also raises scalability, performance, and security concerns based on our study with datasets from operational satellites and 5G. A major challenge is today's stateful mobile core, which suffers from signaling storms in satellites' extreme mobility, intermittent failures in outer space, and attacks when unavoidably exposed to untrusted foreign locations. To this end, we make a case for a stateless mobile core in space. Our solution, SpaceCore, decouples states from orbital core functions, simplifies location states via geospatial addressing, eliminates unnecessary state migrations in satellite mobility by shifting to geospatial service areas, and localizes state retrievals with device-as-the-repository. Our evaluation with datasets from operational satellites and 5G shows SpaceCore's 17.5× over existing solutions signaling reductions and resiliency to failures/attacks.

This paper proposes an improved version of concatenated code scheme in massive random access, with performance enhancement and low-complexity implementation. Under theoretical idealization, most existing solutions only consider improving energy efficiency, ignoring the cost of implementation complexity. However, both of them are vital when it comes to practical realization. Although the concatenated code system is simple to implement, its performance is relatively weak, and the equivalent mapping detection method adopted at the receiving end causes performance loss. Therefore, based on concatenated code system, this paper makes performance improvements in the following three aspects: (i) using multi-level decisions to avoid loss of information; (ii) a priori information aided detection utilizing the distribution of multi-level symbols to minimize the error probability of detection; (iii) soft information output to enhance soft decoders at the next stage. Meanwhile, in decoding complexity, the improved algorithm retains the low complexity feature of the concatenated code system and achieves linear implementation. This scheme successfully enhances energy efficiency at a lower complexity cost by improving the detection algorithm. We prove that the proposed method is an implementation-oriented massive random access with advantages in energy efficiency and performance by theory and simulation.

We devise a simple, provably effective, and readily usable deterrence against intelligent, unknown DDoS threats: Demotivate adversaries to launch attacks via multi-hop traffic divergence. This new strategy is motivated by the fact that existing defenses almost always lag behind numerous emerging DDoS threats and evolving intelligent attack strategies. The root cause is if adversaries are smart and adaptive, no single-hop defenses (including optimal ones) can perfectly differentiate unknown DDoS and legitimate traffic. Instead, we formulate intelligent DDoS as a game between attackers and defenders, and prove how multi-hop traffic divergence helps bypass this dilemma by reversing the asymmetry between attackers and defenders. This insight results in EID, an Economical Intelligent DDoS Demotivation protocol. EID combines local weak (yet divergent) filters to provably null attack gains without knowing exploited vulnerabilities or attack strategies. It incentivizes multi-hop defenders to cooperate with boosted local service availability. EID is resilient to traffic dynamics and manipulations. It is readily deployable with random-drop filters in real networks today. Our experiments over a 49.8 TB dataset from a department at the Tsinghua campus network validate EID's viability against rational and irrational DDoS with negligible costs.

We study a new design objective for "Internet in space" for terrestrial users: To align logical network topology, address, and route in the virtual cyberspace with movements of satellite mega-constellations and earth's rotations in the real physical world. We explain why this is particularly desirable by the recent low-earth-orbit (LEO) mega-constellations, and how it welcomes mobility to streamline the network design for stability, efficiency, and scalability in an unstable space-ground environment. We showcase its feasibility with the common fixed satellite sub-point trajectory in mega-constellations. We describe how it permits stable recursive topology, unifies cyber-physical locations in the address, and naturally embeds the geographical routing into the topological routing.

Here we consider linear regression from the view of correlation and propose a robust regression algorithm. The main idea of this work is from the fact that the inliers lying in a low dimensional subspace are mostly correlated, and the presence of outliers leads to the decrease of correlation. We design an iterative outlier removal algorithm based on correlation, by which the outliers can be effectively removed in a normal-distributed or uniform-distributed data set. Finally, the linear equation is calculated based on the remaining points. The experiment results show that the proposed method outperforms the state-of-the-art approaches. In some cases in which outliers are more than inliers, the proposed method can still obtain the real formulas.

Seamless Internet access under extreme user mobility is highly demanded on high-speed trains and vehicles. However, existing mobile networks (e.g., 4G LTE and 5G NR) cannot reliably satisfy this demand, with a 5.5%-12.6% handover failure ratio at 200–350 km/h. A root cause is that, the 4G/5G handovers have to balance the exploration of more measurements for satisfactory handover and the exploitation for timely handover before the fast-moving user leaves the coverage.We design BaTT, an online learning solution for reliable handovers in extreme mobility. BaTT decomposes the explorationexploitation tradeoff into two multi-armed bandit problems. It uses ϵ-binary-search to optimize the threshold of a serving cell’s signal strength to initiate the handover with $\mathcal{O}(\log J\log T)$ regrets. It further adopts opportunistic Thompson sampling to optimize the sequence of target cells measured for reliable handovers. BaTT can be implemented using the recent Open Radio Access Network (O-RAN) framework in operational 4G LTE and 5G NR. Our evaluations over a dataset from operational LTE networks on the Chinese high-speed rails show a 29.1% handover failure reduction at the speed of 200-350 km/h.

This paper reports our five-year lessons of developing and using MobileInsight, an open-source community tool to enable software-defined full-stack, runtime mobile network analytics inside our phones. We present how MobileInsight evolves from a simple monitor to a community toolset with cross-layer analytics, energy-efficient real-time user-plane analytics, and extensible user-friendly analytics at the control and user planes. These features are enabled by various novel techniques, including cross-layer state machine tracking, missing data inference, and domain-specific cross-layer sampling. Their powerfulness is exemplified with a 5-year longitudinal study of operational mobile network latency using a 6.4TB dataset with 6.1 billion over-the-air messages. We further share lessons and insights of using MobileInsight by the community, as well as our visions of MobileInsight's past, present, and future.

of NB-IoT Based Heterogeneous Wireless Sensor Networks Under Malware Infection" [1], there were errors that have been corrected in the revised version shown below.(i) Related Works (ii) Topology Structure of NBIOT-HWSNs (iii) Malicious Program Propagation Mechanism in NBIOT-HWSNs (iv) Node Availability Analysis of NBIOT-HWSN (v) Numerical Simulation and Analysis (vi) Conclusion (vii) Acknowledgement Section (viii) Figures 1, 2, 3, and 4 have been corrected(ix) Tables 1 and2 have been corrected.The corrected article is as follows.

Extreme mobility has become a norm rather than an exception. However, 4G/5G mobility management is not always reliable in extreme mobility, with non-negligible failures and policy conflicts. The root cause is that, existing mobility management is primarily based on wireless signal strength. While reasonable in static and low mobility, it is vulnerable to dramatic wireless dynamics from extreme mobility in triggering, decision, and execution. We devise REM, Reliable Extreme Mobility management for 4G, 5G, and beyond. REM shifts to movement-based mobility management in the delay-Doppler domain. Its signaling overlay relaxes feedback via cross-band estimation, simplifies policies with provable conflict freedom, and stabilizes signaling via scheduling-based OTFS modulation. Our evaluation with operational high-speed rail datasets shows that, REM reduces failures comparable to static and low mobility, with low signaling and latency cost.

High-speed rail (HSR) systems potentially provide a more efficient way of door-to-door transportation than airplane. However, they also pose unprecedented challenges in delivering seamless Internet service for on-board passengers. In this paper, we conduct the first large-scale disconnection-centric measurement study of TCP performance over LTE on HSR. Our measurement targets the main HSR route in China operating at 300/350 km/h. We performed extensive data collection obtaining 378.3 GB data collected over 56639 km of trips. Leveraging such a unique dataset, we measure important performance metrics such as TCP goodput, latency and loss rate across different congestion control algorithm, mobile carrier, and different train speed. We further develop the LTE disconnection taxonomy, and conduct a in-depth correlation study between TCP stall and LTE disconnection. Our findings reveal the networking performance on today's HSR environment “in the wild”, as well as identify several root causes of performance inefficiencies, which together highlight the need to develop dedicated protocol mechanisms that are friendly to extreme mobility.

High-speed rail (HSR) systems potentially provide a more efficient way of door-to-door transportation than airplane. However, they also pose unprecedented challenges in delivering seamless Internet service for on-board passengers. In this paper, we conduct a large-scale active-passive measurement study of TCP performance over LTE on HSR. Our measurement targets the HSR routes in China operating at above 300 km/h. We performed extensive data collection through both controlled setting and passive monitoring, obtaining 1732.9 GB data collected over 135719 km of trips. Leveraging such a unique dataset, we measure important performance metrics such as TCP goodput, latency, loss rate, as well as key characteristics of TCP flows, application breakdown, and users' behaviors. We further quantitatively study the impact of frequent cellular handover on HSR networking performance, and conduct in-depth examination of the performance of two widely deployed transport-layer protocols: TCP CUBIC and TCP BBR. Our findings reveal the performance of today's commercial HSR networks "in the wild'', as well as identify several performance inefficiencies, which motivate us to design a simple yet effective congestion control algorithm based on BBR to further boost the throughput by up to 36.5%. They together highlight the need to develop dedicated protocol mechanisms that are friendly to extreme mobility.

The 4G/5G cellular edge promises low-latency experiences anywhere, anytime. However, data charging gaps can arise between the cellular operators and edge application vendors, and cause over-/under-billing. We find that such gap can come from data loss, selfish charging, or both. It can be amplified in the edge, due to its low-latency requirements. We devise TLC, a Trusted, Loss-tolerant Charging scheme for the cellular edge. In its core, TLC enables loss-selfishness cancellation to bridge the gap, and constructs publicly verifiable, cryptographic proof-of-charging for mutual trust. We implement TLC with commodity edge nodes, OpenEPC and small cells. Our experiments in various edge scenarios validate TLC's viability of reducing the gap with marginal latency and other overhead.

The Narrowband Internet of Things (NB-IoT) is a main stream technology based on mobile communication system. The combination of NB-IoT and WSNs can active the application of WSNs. In order to evaluate the influence of node heterogeneity on malware propagation in NB-IoT based Heterogeneous Wireless Sensor Networks, we propose a node heterogeneity model based on node distribution and vulnerability differences, which can be used to analyze the availability of nodes. We then establish the node state transition model by epidemic theory and Markov chain. Further, we obtain the dynamic equations of the transition between nodes and the calculation formula of node availability. The simulation result is that when the degree of node is small and the node vulnerability function is a power function, the node availability is the highest; when the degree of node is large and the node vulnerability function satisfies the exponential function and the power function, the node availability is high. Therefore, when constructing a NBIOT-HWSNs network, node protection is implemented according to the degree of node, so that when the node vulnerability function satisfies the power function, all nodes can maintain high availability, thus making the entire network more stable.

Multi-panel MIMO is a promising technology in millimeter wave communications. Due to its partially hybrid structure and non-uniform antenna array, existing channel estimation and hybrid precoding cannot be directly applied to multipanel MIMO. In this paper, we study channel estimation and hybrid precoding for multi-panel MIMO. We first transform the channel response vector into angular domain and then reconstruct channel state information (CSI) by using the estimated angular CSI. Moreover, by exploiting the sparse nature of mmWave channels, a sparsity-based channel estimation method is proposed to reduce training overhead and the computational complexity of hybrid precoding. Numerical results show that the proposed channel estimation and hybrid precoding scheme achieve satisfactory spectral efficiency performance with greatly reduced training overhead and low computational complexity.

In this paper, we present our recent work in progress on 4G mobile network analysis. In order to provide an in-depth study on the closed network operations, we advocate a novel approach via two-level, device-centric machine learning that can open up the system behaviors and facilitate fine-grained analysis . We describe our proposed approach, and use the latency analysis on two popular mobile apps (Web browsing and Instant Messaging) to illustrate how our scheme works. We further preliminary results and discuss the open issues.

This work presents a novel variable stiffness structure consisting of a fiber-constrained dielectric elastomer tube filled with insulating oil. The tensile stiffness of the structure can be adjusted by voltages and its initial value can be customized according to the initial pre-stretch of the material. The structure has a dimension of ∼30 mm diameter × 50 mm length. A mathematical analysis is established to predict the initial tensile stiffness of the structure. The changes of the tensile stiffness of the structure under voltages are verified experimentally. The results show a decrease of the tensile stiffness of the device by 25% at 4 kV and the decrement is also related to the elongation of the structure. With different pre-stretches and dimensions of the dielectric elastomer, one can obtain devices with different variation ranges of tensile stiffness.

The minimum weight vertex cover (MWVC) problem is an important combinatorial optimization problem with various real-world applications. Due to its NP hardness, most works on solving MWVC focus on heuristic algorithms that can return a good quality solution in reasonable time. In this work, we propose two dynamic strategies that adjust the behavior of the algorithm during search, which are used to improve a state of the art local search for MWVC named FastWVC, resulting in two local search algorithms called DynWVC1 and DynWVC2. Previous MWVC algorithms are evaluated on graphs with random or hand crafted weights. In this work, we evaluate the algorithms on the vertex weighted graphs that obtained from an important real world problem, the map labeling problem. Experiments show that our algorithm obtains better results than previous algorithms for MWVC and maximum weight independent set (MWIS) on these real world instances. We also test our algorithms on massive graphs studied in previous works, and show significant improvements there.

Multi-carrier cellular access dynamically selects a preferred wireless carrier by leveraging the availability and diversity of multiple carrier networks at a location. It offers an alternative to the dominant single-carrier paradigm, and shows early signs of success through the operational Project Fi by Google. In this paper, we study the important, yet largely unexplored, problem of inter-carrier switching for multi-carrier access. We show that policy conflicts can arise between inter- and intra-carrier switching, resulting in oscillations among carriers in the worst case akin to BGP looping. We derive the conditions under which such oscillations occur for three categories of popular policy, and validate them with Project Fi whenever possible. We provide practical guidelines to ensure loop-freedom and assess them via trace-driven emulations.

In this paper, we introduce a joint antenna selection and user sorting scheme in multi-user multiple-input multiple-output (MU-MIMO) systems, in which Tomlinson-Harashima precoding (THP) is adopted. In MU-MIMO with THP, antenna selection and user sorting are important means to improve the system performance. By considering the interrelationship between antenna selection and user sorting in THP, based on the scheme, we propose an efficient algorithm to solve the antenna selection and user sorting jointly, which achieves near performance of the optimal one obtained by exhaustive search. Analytical results demonstrate the proposed scheme has low complexity. Simulation results show the availability of our proposed scheme.

In this paper, a partial channel state information (CSI) based robust Tomlinson-Harashima precoding (THP) transceiver is proposed in downlink time division duplexing (TDD) multi-user multiple-input multiple-output (MU-MIMO) systems. Partial CSI is obtained only based on the observations of uplink pilot training. Our robust THP transceiver includes THP matrices and a Demodulation Reference Signal (DMRS) precoding matrix. By minimizing the mean square error of the received signal at the user equipment (UE) side, robust THP matrices can be optimized at the BS. The optimized robust THP receiving matrices are transmitted to the UEs by DMRS. Based on the optimized THP matrices, DMRS precoder is also designed with partial CSI to make sure that the UEs can recover the receiving matrices as precise as possible. Simulation results show that our proposed THP transceiver is robust to both the partial CSI and the imperfect receiving matrices.

In this paper, we study full-dimension multiple-input multiple-output (FD-MIMO) systems where a base station is equipped with multiple panel array (MPA) antennas. As the MPA is regarded as a promising means of practical implementation for the FD-MIMO, it is important to characterize a channel modeling and a codebook design. Thus, we first examine the exponential correlation model and the three-dimensional correlation model for the FD-MIMO with MPA. In addition, since antenna elements are not uniformly spaced in the MPA systems, a discrete Fourier transform (DFT) codebook may not be suitable due to phase ambiguity (PA). Thus, we investigation three new codebook design methods which reflect the PA. In numerical results, we confirm that PA compensation and per-panel quantization are effective for MPA systems.

Accessing multiple carrier networks (T-Mobile, Sprint, AT&T, and so on) offers a promising paradigm for smartphones to boost its mobile network quality. However, the current practice does not achieve the full potential of this approach because it has not utilized fine-grained, cellular-specific domain knowledge. Our experiments and code analysis discover three implementation-independent issues: 1) it may not trigger the anticipated switch when the serving carrier network is poor; 2) the switch takes a much longer time than needed; and 3) the device fails to choose the high-quality network (e.g., selecting 3G rather than 4G). To address them, we propose iCellular, which exploits low-level cellular information at the device to improve multi-carrier access. iCellular is proactive and adaptive in its multi-carrier selection by leveraging existing end-device mechanisms and standards-complaint procedures. It performs adaptive monitoring to ensure responsive selection and minimal service disruption and enhances carrier selection with online learning and runtime decision fault prevention. It is readily deployable on smartphones without infrastructure/hardware modifications. We implement iCellular on commodity phones and harness the efforts of Project Fi to assess multi-carrier access over two U.S. carriers: T-Mobile and Sprint. Our evaluation shows that, iCellular boosts the devices' throughput with up to 3.74× throughput improvement, 6.9× suspension reduction, and 1.9× latency decrement over the state of the art, with moderate CPU, and memory and energy overheads.

In multiagent systems (MASs), agents need to forward packets to each other to accomplish a target task. In this paper, we study packet forwarding among agents using evolutionary game theory under the mechanisms of Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA). Packet forwarding among agents plays a key role to stabilize the whole MAS. We study the transfer probability of packet forwarding of agents at the idle state or the busy state and computer the probability of the packet forwarding for a MAS. When agents make their decisions to selectForwardorNo-Forwardstrategy, a packet forwarding evolutionary game model is built to reflect the utilities of different packet forwarding strategies. Two incentive mechanisms are introduced into the game model. One is to motivate agents to strengthen cooperation; the other is to encourage agents to select theNo-Forwardstrategy to save energy while they are in the busy state. The parameter value that encourages an agent to select the No‐Forward strategy is inversely proportional to the average probability of the packet forwarding. The replicator dynamics of agent packet forwarding strategy evolution are given. We propose and prove the theorems indicating that evolutionarily stable strategies (ESSs) can be attained. The results of simulation experiments verify the correctness of the proposed theorems and the effects of the two incentive mechanisms and the probability of packet forwarding, which assures the robustness of evolutionary stable points among agents in MASs.

Large-scale antenna arrays which provide high beamforming gain are commonly used to combat the serious path-loss in millimeter wave (mmwave) systems. Traditionally, the beamforming is completely implemented at the baseband or the digital domain, which, however, causes high hardware cost and power consumption. The hybrid precoding method which can effectively avoids these problems is, therefore, more attractive. However, the design of hybrid precoder is challenging due to the constant modules of phase shifters. To solve this problem, we propose a novel hybrid precoding approach from the perspective of geometric construction in this paper. The new method can significantly reduce the number of RF chains meanwhile still achieve an almost optimal performance in terms of sum rate. The proposed algorithm is compared with the popular orthogonal matching pursuit algorithm (OMP) via numerical simulations, and shows that our proposal can increase the system spectral efficiency with reduced computational complexity.

In this paper, we call for a paradigm shift away from the wireless-access focused research efforts on 5G networked systems. We believe that the architectural limitations should share equal blame on issues of performance, reliability, and security. We thus identify architectural weakness on both sides of the mobile clients and the 4G network infrastructure. Our recent findings show that, contrary to commonly held perceptions, many design and operational issues arise not due to poor wireless link qualities. Instead, they are rooted in such architectural downsides. To address these issues, we further propose a new approach of enabling automated intelligence inside the 4G/5G network systems. We next describe our ongoing efforts along two dimensions: empowering date-driven smart clients and constructing verifiable network infrastructure. We report some early results and discuss possible next steps.

This paper presents an inchworm inspired soft robot composed of the soft body, the front foot as well as the back foot. Compared to the traditional inchworm-type robot consisting of rigid components, the driven mode for the soft robot is more simple. The soft robot inspired by the inchworm has higher locomotion efficiency than the other bionic soft robot. The main idea of this paper is to imitate the “Ω” motion shape of biology inchworm based on a silicone square tube with strain-limiting layers. Besides, each foot of the robot made through 3D printing technology together with metal sheet can produce different friction coefficients to achieve the anchor-motion movement. Then, the robot realizes an inchworm-like locomotion under certain actuation patterns. Experimental results show that the proposed robot has excellent performance.

Control-plane operations are indispensable to providing data access to mobile devices in the 4G LTE networks. They provision necessary control states at the device and network nodes to enable data access. However, the current design may suffer from long data access latency even under good radio conditions. The fundamental problem is that, data-plane packet delivery cannot start or resume until all control-plane procedures are completed, and these control procedures run sequentially by design. We show both are more than necessary under popular use cases. We design DPCM, which reduces data access latency through parallel processing approaches and exploiting device-side state replica. We implement DPCM and validate its effectiveness with extensive evaluations.

Tick is a new SDR system that provides programmability and ensures low latency at both PHY and MAC. It supports modular design and element-based programming, similar to the Click router framework [23]. It uses an accelerator-rich architecture, where an embedded processor executes control flows and handles various MAC events. User-defined accelerators offload those tasks, which are either computation-intensive or communication-heavy, or require fine-grained timing control, from the processor, and accelerate them in hardware. Tick applies a number of hardware and software co-design techniques to ensure low latency, including multi-clock-domain pipelining, field-based processing pipeline, separation of data and control flows, etc. We have implemented Tick and validated its effectiveness through extensive evaluations as well as two prototypes of 802.11ac SISO/MIMO and 802.11a/g full-duplex.

SMS (Short Messaging Service) is a text messaging service for mobile users to exchange short text messages. It is also widely used to provide SMS-powered services (e.g., mobile banking). With the rapid deployment of all-IP 4G mobile networks, the underlying technology of SMS evolves from the legacy circuit-switched network to the IMS (IP Multimedia Subsystem) system over packet-switched network. In this work, we study the insecurity of the IMS-based SMS. We uncover its security vulnerabilities and exploit them to devise four SMS attacks: silent SMS abuse, SMS spoofing, SMS client DoS, and SMS spamming. We further discover that those SMS threats can propagate towards SMS-powered services, thereby leading to three malicious attacks: social network account hijacking, unauthorized donation, and unauthorized subscription. Our analysis reveals that the problems stem from the loose security regulations among mobile phones, carrier networks, and SMS-powered services. We finally propose remedies to the identified security issues.

Handoff is a critical mechanism in cellular networks. When the mobile device moves out of the coverage of the serving cell (i.e., base station), a handoff is performed to switch its serving cell to another and thus to ensure seamless network access. To provide nice user experience, it is desirable to select the preferred cell (e.g., 4G rather than 3G/2G in most cases) among multiple candidate cells which all are around and able to serve the device if needed. In this paper, we examine the property of desired reachability in the current design and practice of handoff. We show that handoff is designated to be configurable in order to accommodate diverse requirements by users and operators. However, handoff misconfigurations exist and they make the device stuck in an undesired target cell (e.g., 2G when 4G available). We model the distributed mobility management as an iterative process and use a formal analysis to classify the causes. We further design a software tool to detect handoff misbehaviors and run it over operational networks. We validate the identified issues on two major US mobile carrier networks.

Femtocells (small cells) augment the current mobile network by providing users short-range radio access at home and small-business settings. They have rapidly emerged as a promising scheme to alleviate capacity and coverage shortage by offloading traffic from the conventional Macrocells (large cells). Despite its increasing popularity, the real-world Femtocell performance has remained largely unexplored. In this paper, we conduct an in-depth study to assess Femtocell performance and diagnose identified issues in operational carrier networks. We focus on user-deployed Femtocells in a top-tier US mobile network. While the Femtocell generally works well, unanticipated performance degradations and even failures still occur. Contrary to conventional wisdom in the research community, we find that, radio link quality and interference is not the main bottleneck of Femtocells in many real-life usage scenarios. For instance, while Femtocell deployment at blind-zones with no radio coverage is desirable, not all deployments have succeeded; Compared with their Macrocell counterparts, Femtocells exhibit lower speed and larger speed variations, and induce larger delay for data services. Moreover, mobility support for femtocells is incomplete and no seamless migration is available under certain usage scenarios. We pinpoint their root causes, quantify the potential impacts, and share the learned lessons.

We present the demonstration of MobileInsight, a software tool that collects, analyzes and exploits runtime information from operational cellular network. MobileInsight runs on commercial off-the-shelf phones without extra hardware or additional support from cellular network operators. It exposes cellular protocol messages from the 3G/4G chipset, and performs in-device protocol analysis. We demonstrate the in-device runtime cellular message collection, analysis of the protocol states, visualization of runtime wireless channel and mobility dynamics, and how mobile applications benefit from MobileInsight.

We design and implement MobileInsight, a software tool that collects, analyzes and exploits runtime network information from operational cellular networks. MobileInsight runs on commercial off-the-shelf phones without extra hardware or additional support from operators. It exposes protocol messages on both control plane and (below IP) data plane from the 3G/4G chipset. It provides in-device protocol analysis and operation logic inference. It further offers a simple API, through which developers and researchers obtain access to low-level network information for their mobile applications. We have built three showcases to illustrate how MobileInsight is applied to cellular network research.

Mobility support is critical to offering seamless data service to mobile devices in 3G/4G cellular networks. To accommodate policy requests by users and carriers, micro-mobility management scheme among cells (i.e., handoff) is designated to be configurable. Each cell and mobile device can configure or even customize its own handoff procedure. In this paper, we examine the handoff misconfiguration issues in 3G/4G networks. We show that they may incur handoff instability in the form of persistent loops, where the device oscillates between cells even without radio-link and location changes. Such instability is mainly triggered by uncoordinated parameter configurations and inconsistent decision logic in the hand- off procedure. It can degrade user data performance, incur excessive signaling overhead, and violate network's expected handoff goals. We derive the instability conditions, and validate them on two major US mobile carrier networks. We further design a soft- ware tool for automatic loop detection, and run it over operational networks. We discuss possible fixes to such uncoordinated configurations among devices and cells.

The control-plane protocols in 3G/4G mobile networks communicate with each other, and provide a rich set of control functions, such as radio resource control, mobility support, connectivity management, to name a few. Despite their significance, the problem of verifying protocol correctness remains largely unaddressed. In this paper, we examine control-plane protocol interactions in mobile networks. We propose CNetVerifier, a two-phase signaling diagnosis tool to detect problematic interactions in both design and practice. CNetVerifier first performs protocol screening based on 3GPP standards via domain-specific model checking, and then conducts phone-based empirical validation in operational 3G/4G networks. With CNetVerifier, we have uncovered seven types of troublesome interactions, along three dimensions of cross (protocol) layers, cross (circuit-switched and packet-switched) domains, and cross (3G and 4G) systems. Some are caused by necessary yet problematic cooperation (i.e., protocol interactions are needed but they misbehave), whereas others are due to independent yet unnecessary coupled operations (i.e., protocols interactions are not required but actually coupled). These instances span both design defects in 3GPP standards and operational slips by carriers and vendors. They all result in performance penalties or functional incorrectness. We deduce root causes, present empirical results, propose solutions, and summarize learned lessons.

VoLTE (Voice-over-LTE) is the designated voice solution to the LTE mobile network, and its worldwide deployment is underway. It reshapes call services from the traditional circuit-switched telecom telephony to the packet-switched Internet VoIP. In this work, we conduct the first study on VoLTE security before its full rollout. We discover several vulnerabilities in both its control-plane and data-plane functions, which can be exploited to disrupt both data and voice in operational networks. In particular, we find that the adversary can easily gain free data access, shut down continuing data access, or subdue an ongoing call, etc. We validate these proof-of-concept attacks using commodity smartphones (rooted and unrooted) in two Tier-1 US mobile carriers. Our analysis reveals that, the problems stem from both the device and the network. The device OS and chipset fail to prohibit non-VoLTE apps from accessing and injecting packets into VoLTE control and data planes. The network infrastructure also lacks proper access control and runtime check.

Control-plane protocols are complex in cellular networks. They communicate with one another along three dimensions of cross layers, cross (circuit-switched and packet-switched) domains, and cross (3G and 4G) systems. In this work, we propose signaling diagnosis tools and uncover six instances of problematic interactions. Such control-plane issues span both design defects in the 3GPP standards and operational slips by carriers. They are more damaging than data-plane failures. In the worst-case scenario, users may be out of service in 4G, or get stuck in 3G. We deduce root causes, propose solutions, and summarize learned lessons.

Recently, Information Centric Networking (ICN) has attracted much attention also for mobiles. Unlike host-based communication models, ICN promotes data names as the first-class citizen in the network. However, the current ICN name-based routing requires Interests be routed by name to the nearest replica, implying the Interests are flooded in VANET. This introduces large overhead and consequently degrades wireless network performance. In order to maintain the efficiency of ICN implementation in VANET, we propose an opportunistic geo-inspired content based routing method. Our method utilizes the last encounter information of each node to infer the locations of content holders. With this information, the Interests can be geo-routed instead of being flooded to reduce the congestion level of the entire network. The simulation results show that our proposed method reduces the scope of flooding to less than two hops and improves retrieval rate by 1.42 times over flooding-based methods.

This paper addresses the analog feedback of channel state information (CSI) in multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) based wideband wireless communication systems. In MIMO-OFDM systems, the amount of CSI to be fed back grows prohibitively with the number of subcarriers. Aiming at reducing the feedback overhead, we design efficient analog feedback schemes. In our approach, the user equipment (UE) samples the CSI so that only a small fraction of the CSI needs to be fed back to the base station (BS). Based on the sampled feedback information, the BS reconstructs full CSI using some interpolation method. Low complexity clustering/interpolation based schemes are first considered, but these schemes only provide limited feedback quality. To improve the performance, we analyze the sparsity of the time-domain channel impulse response and propose a time-domain filtering scheme based on the sparsity property. Simulation results show that the time-domain filtering scheme efficiently decreases the interpolation distortion and feedback error, and achieves much better performance than the clustering/interpolation based schemes.

Multicast benefits group communications in saving network traffic and improving application throughput, both of which are important for data center applications. However, the technical trend of data center design poses new challenges for efficient and scalable multicast routing. First, the densely connected networks make traditional receiver-driven multicast routing protocols inefficient in multicast tree formation. Second, it is quite difficult for the low-end switches widely used in data centers to hold the routing entries of massive multicast groups. In this paper, we propose ESM, an efficient and scalable multicast routing scheme for data center networks. ESM addresses the challenges above by exploiting the feature of modern data center networks. Based on the regular topology of data centers, ESM uses a source-to-receiver expansion approach to build efficient multicast trees, excluding many unnecessary intermediate switches used in receiver-driven multicast routing. For scalable multicast routing, ESM combines both in-packet Bloom Filters and in-switch entries to make the tradeoff between the number of multicast groups supported and the additional bandwidth overhead. Simulations show that ESM saves 40%$\sim$50% network traffic and doubles the application throughputs compared to receiver-driven multicast routing, and the combination routing scheme significantly reduces the number of in-switch entries required. We implement ESM on a Linux platform. The experimental results further demonstrate that ESM can well support online tree building for large-scale groups with churns, and the overhead of the combination forwarding engine is light-weighted.