SPLICE: Secure Predictive Low-Latency Information Centric Edge for Next Generation Wireless Networks
List of Personnel
1. P. R. Kumar; Texas A&M Engineering Experiment Station; PI
2. Srinivas Shakkottai; Texas A&M Engineering Experiment Station; Co-PI
3. I-Hong Hou; Texas A&M Engineering Experiment Station; Co-PI
4. Ness Shroff; The Ohio State University; Co-PI
5. Atilla Eryilmaz; The Ohio State University; Co-PI
6. Patrick Crowley; Washington University in Saint Louis; Co-PI
7. Y . Charlie Hu; Purdue University; Co-PI
8. Elisa Bertino; Purdue University; Co-PI
9. Romit Roy Choudhury; University of Illinois at Urbana-Champaign; Co-PI
The goals of the proposed research will be conducted across three major inter-related thrusts:
I) Applications for an Information Centric Wireless Edge: This thrust will define the performance criteria and determine the architecture of the two main focus applications of our project: VR/AR and UAV control, and help to orient our research in terms of satisfying their information and latency needs. Key to an effective solution is to exploit multiple dimensions of commonality between instances of these applications through fast content caching and retrieval, and multicast services provided by an NDN- WEN.
II) Information Centric Networking for the Wireless Edge: This thrust will explore how to move NDN into the wireless domain, forming a layer connecting the Communication Plane to the Application Plane. Our major goals are to design the NDN-WEN architecture, how information exchange via caching would be accomplished, and how to design secure data transfer methods. A key novelty is the use of proactive secure caching that learns the value of applications and ensured they are cached ahead of demand.
III) Wireless Communication for Information Centric Networks: We aim at developing the interface between the backbone and the wireless edge capabilities, for low-overhead, low-delay, and heterogeneous service requirements. Key to this goal will be the successful solution of the question of how to employ multicasting capabilities of wireless communication while simultaneously achieving low-delay guarantees and low-complexity feedback mechanisms, via a mix of coding, caching and scheduling techniques.
- An increasing number of applications that will be supported by next generation wireless networks require packets to arrive before a certain deadline for the system to have the desired performance. While many time-sensitive scheduling protocols have been pro- posed, few have been experimentally evaluated to establish realistic performance. Furthermore, some of these protocols involve high complexity algorithms that need to be performed on a per-packet basis. Experimental evaluation of these protocols requires a flexible platform that is readily capable of implementing and experimenting with these protocols. We present PULS, a processor-supported ultra low latency scheduling implementation for testing of downlink scheduling protocols with ultra-low latency requirements. Based on our decoupling architecture, programmability of delay sensitive scheduling protocols is done on a host machine, with low latency mechanisms being deployed on hardware. This enables flexible scheduling policies on software and high hardware function re-usability, while meeting the timing requirements of a MAC. We performed extensive tests on the platform to verify the latencies experienced for per packet scheduling, and present results that show packets can be scheduled and transmitted under 1 ms in PULS.
- Typical analysis of content caching algorithms using the metric of steady state hit probability under a stationary request process does not account for performance loss under a variable request arrival process. In this work, we instead conceptualize caching algorithms as complexity-limited online distribution learning algorithms, and use this vantage point to study their adaptability from two perspectives: (a) the accuracy of learning a fixed popularity distribution; and (b) the speed of learning items’ popularity. In order to attain this goal, we compute the distance between the stationary distributions of several popular algorithms with that of a genie-aided algorithm that has knowledge of the true popularity ranking, which we use as a measure of learning accuracy. We then characterize the mixing time of each algorithm, i.e., the time needed to attain the stationary distribution, which we use as a measure of learning efficiency. We merge both measures above to obtain the “learning error” representing both how quickly and how accurately an algorithm learns the optimal caching distribution, and use this to determine the trade-off between these two objectives of many popular caching algorithms.
- In networked cyber-physical systems, the interdelivery time of data packets becomes an important quantity of interest. However, providing a guarantee that the inter-delivery times of the packets are “small enough” becomes a difficult task in such systems due to the unreliable communication medium and limited network resources. We design scheduling policies that meet the inter-delivery time requirements of multiple clients connected over wireless channels. We formulate the problem as an infinite-state risk-sensitive Markov decision process, where large exceedances of inter-delivery times for different clients over their design thresholds are severely penalized. We reduce the infinite-state problem to an equivalent finite-state problem and establish the existence of a stationary optimal policy and an algorithm for computing it in a finite number of steps. However, its computational complexity makes it intractable when the number of clients is of the order of 100 or so that is found in applications such as in-vehicle networks. To design computationally efficient optimal policies, we, therefore, develop a theory based on the high reliability asymptotic scenario, in which the channel reliability probabilities are close to one. We thereby obtain an algorithm of relatively low computational complexity for determining an asymptotically optimal policy. To address the remaining case when the channels are not relatively reliable, we design index-based policies for the risk sensitive case, which extends key ideas for index policies in risk-neutral multi-armed bandit problems. Simulation results are provided to show the effectiveness of our policies.
- The vast amount of spectrum available at millimeter-wave bands has made millimeter-wave communications one of the key enabling technologies of the fifth-generation cellular network. The high directionality of the transmitter and the receivers operating at millimeter-wave frequencies introduces certain novel challenges for medium access control. Specifically, in a scenario where there is relative motion between the transmitter and the receivers, the transmitter must keep track of the direction in which each received signal is, and the receivers must keep track of where the transmitter is, so that they can orient their antenna boresight towards each other, or in a non-line-of-sight environment, in directions that optimize the link gain, in order to establish a physical link. In this paper, we propose TrackMAC, a directional medium access control protocol for millimeter-wave local area networks, that allows an access point to efficiently track every station associated with it at small overheads. The proposed protocol can be implemented squarely within the specifications of the IEEE 802.11ad standard for millimeter-wave local area networking.
- We consider multihop networks serving multiple flows in which packets have hard deadlines. Packets not delivered to their destinations by their deadlines are of no value. The throughput of packets delivered within their deadlines is called the timely throughput. We address the design of packet scheduling, transmit power control, and routing policies that maximize any specified weighted average of the timely throughputs of the multiple flows. We determine a tractable linear program (LP) whose solution yields an optimal routing, scheduling, and power control policy, when nodes have average-power constraints. The optimal policy is fully decentralized, with decisions regarding any packet’s transmission scheduling, transmit power level, and routing, based solely on the age and location of that packet. No knowledge of states of any other packets in the network is needed. This resolves a fundamental obstacle that arises whenever one attempts to optimally schedule networks. The number of variables in the LP is bounded by the product of the square of the number of nodes, the number of flows, the maximum relative deadline, and the number of transmit power levels. This solution is obtained from decomposition of the Lagrangian of the constrained Markov decision process describing the complete network state. Global coordination is achieved through a price for energy usage paid by a packet each time that its transmission is attempted at a node. It is fundamentally different from the decomposition of the fluid model used to derive the backpressure policy, which is throughput optimal when packets have no deadlines, where prices are related to queue lengths. If nodes instead have peak-power constraints, then a decentralized policy obtained by simple truncation is near optimal as link capacities increase in a proportional way.
- The scarcity of spectrum in sub-6 GHz frequency bands to meet projected wireless traffic demands has led to the wireless industry incorporating millimeter-wave technology in the design of next-generation wireless systems. The high path loss of millimeter-wave signals necessitates radios that operate in these frequencies to employ highly directional beams for transmission and reception. The transition from traditional omni-directional transmission and reception to highly directional links has drastic implications on the Medium Access Control (MAC) design, since it shifts the objective of the MAC layer from proactive interference avoidance to transmitter-receiver coordination to achieve beam alignment. In this paper, we present IRIS, a directional MAC protocol for mm-wave ad-hoc mobile networks that achieves this objective. Specifically, the Iris protocol is designed to distributedly coordinate the nodes so that transmitters and their intended receivers align their antenna boresights to establish a physical link between them when required. We establish certain performance guarantees that the Iris protocol provides, and illustrate the design process of a mm-wave ad-hoc MAC based on the Iris protocol.
- We propose online scheduling policies to optimize quality of experience (QoE) for video-on-demand applications in wireless networks. We consider wireless systems where an access point (AP) transmits video content to clients over fading channels. The QoE of each flow is measured by its duration of video playback interruption. We are specifically interested in systems operating in the heavy-traffic regime. We first consider a special case of ON-OFF channels plus constant-bit-rate videos and establish a scheduling policy that achieves every point in the capacity region under heavy-traffic conditions. This policy is then extended for more general fading channels and variable-bit-rate videos, and we prove that it remains optimal under some mild conditions.
- Edge servers, which are small servers located close to mobile users, have the potential to greatly reduce delay and backhaul traffic of mobile Internet applications by moving cloud services to the edge of the network. Due to limited capacity of edge servers and dynamic request arrival, proper service caching at the edge is essential to guarantee good performance. We propose a tractable online algorithm called retrospective download with least-requested deletion (RED/LED) that caches services dynamically without any assumptions on the arrival patterns of mobile applications. We evaluate the competitive ratio of our policy, which quantifies the worst-case performance in comparison to an optimal offline policy. We prove that the competitive ratio of our policy is linear with the capacity of the edge server. We also show that no deterministic online policy can achieve a competitive ratio that is asymptotically better than ours. Moreover, we prove that our policy is scalable, in the sense that it only needs doubled capacity to achieve a constant competitive ratio.
- We propose a practical non-monetary mechanism that induces the efficient solution to the optimal rate control problem, where each client optimizes its request arrival rate to maximize its own net utility individually, and at the Nash Equilibrium the total net utility of the system is also maximized. Existing mechanisms typically rely on monetary exchange which requires additional infrastructure that is not always available. Instead, the proposed mechanism is based on efficient cost allocation, where the cost is in terms of nonmonetary metric such as average delay or request loss rate. Specifically, we present an efficient cost allocation rule for the server to determine the target cost of each client. We then propose an intelligent policy for the server to control the costs of the clients to achieve the efficient allocation. Furthermore, we design a distributed rate control protocol with provable convergence to the Nash Equilibrium of the system.
- We study the problem of allocating jobs to appropriate servers in cloud computing. We consider that jobs of various types arrive in some unpredictable pattern and the system is required to allocate a certain ratio of jobs. In order to meet the hard allocation ratio requirement in the presence of unknown arrival patterns, one can increase the capacity of servers by expanding the size of data centers. We then aim to find the minimum capacity needed to meet a given allocation ratio requirement. We study this problem for both systems with persistent jobs, such as video streaming, and systems with dynamic jobs, such as database queries.
- We consider a wireless network where multiple flows are delivering status updates about their respective information sources. An end user aims to make accurate real-time estimations about the status of each information source using its received packets. As the accuracy of estimation is most impacted by events in the recent past, we propose to measure the credibility of an information flow by the number of timely deliveries in a window of the recent past, and say that a flow suffers from a loss-of-credibility (LoC) if this number is insufficient for the end user to make an accurate estimation. We then study the problem of minimizing the system-wide LoC in wireless networks where each flow has different requirement and link quality.
- We propose a feasibility-optimal decentralized algorithm for real-time wireless ad hoc networks, where a strict deadline is imposed for each packet. While centralized scheduling algorithms provide provably optimal theoretical guarantees, they may not be practical in many settings, such as industrial networked control systems. Therefore, it is of great importance to design an algorithm that achieves feasibility optimality in a decentralized manner. To design a decentralized algorithm, we leverage two widely-used functions of wireless devices: carrier sensing and backoff timers. Different from the conventional approach, the proposed algorithm utilizes a collision-free backoff scheme to enforce the transmission priority of different links. This design obviates the capacity loss due to collision with quantifiably small backoff overhead. The algorithm is fully decentralized in the sense that every link only needs to know its own priority, and links contend for priorities only through carrier sensing.
- We provide a comprehensive analysis of stability properties and delay gains that wireless multicasting capabilities, as opposed to more traditional unicast transmissions, can provide for content distribution in mobile networks. In particular, we propose a model and characterize the average queue-length (and hence average delay) performance of unicasting and various multicasting strategies for serving a dynamic user population at the wireless edge. First, we show that optimized static randomized multicasting (we call it ‘blind multicasting’) leads to stable-everywhere operation irrespective of the network loading factor (given by the ratio of the demand rate to the service rate) and the content popularity distribution. In contrast, traditional unicasting suffers from unstable operation when the loading factor approaches one, although it outperforms blind multicasting at small loading factor levels. This motivates us to study ‘work-conserving multicast’ policies next that always outperform unicasting while still offering stable-everywhere operation. Then, in the worst-case of uniformly-distributed content popularity, we explicitly characterize the scaling of the average queue-length (and hence delay) under a first-come-first-serve multicast strategy as a function of the database size and the loading factor.
- Least-recently-used (LRU) caching and its variants have conventionally been used as a fundamental and critical method to ensure fast and efficient data access in computer and communication systems. Emerging data-intensive applications over unreliable channels, e.g., mobile edge computing and wireless content delivery networks, have imposed new challenges in optimizing LRU caching systems in environments prone to failures. Most existing studies focus on reliable channels, e.g., on wired Web servers and within data centers, which have already yielded good insights with successful algorithms on how to reduce cache miss ratios. Surprisingly, we show that these widely held insights do not necessarily hold true for unreliable channels. We consider a single-hop multi-cache distributed system with data items being dispatched by random hashing. The objective is to achieve efficient cache organization and data placement. The former allocates the total memory space to each of the involved caches. The latter decides data routing strategy and data replication scheme. Analytically we characterize the unreliable LRU caches by explicitly deriving their asymptotic miss probabilities. Based on these results, we optimize the system design. Remarkably, these results sometimes are counterintuitive, differing from the ones obtained for reliable caches. We discover an interesting phenomenon: asymmetric cache organization is optimal even for symmetric channels. Specifically, even when cache unreliability probabilities are equal, allocating the cache spaces unequally can achieve a better performance. We also propose an explicit unequal allocation policy that outperforms the equal allocation. In addition, we prove that splitting the total cache space into separate LRU caches can achieve a lower asymptotic miss probability than resource pooling that organizes the total space in a single LRU cache.
- We consider the problem of scheduling in wireless networks with the aim of maintaining up-to-date and synchronized (also called, aligned) information at the receiver across multiple flows. This is in contrast to the more conventional approach of scheduling for optimizing long-term performance metrics such as throughput, fairness, or average delay. Maintaining the age of information at a low and roughly equal level is particularly important for distributed cyber-physical systems, in which the effectiveness of the control decisions depends critically on the freshness and synchrony of information from multiple sources/sensors. In this work, we first expose the weakness of several popular MaxWeight scheduling solutions that utilize queue-length, delay, and age information as their weights. Then, we develop a novel age-based scheduler that combines age with the interarrival times of incoming packets in its decisions, which yields significant gains in the information freshness at the receiver. We characterize the performance of our strategy through a heavy-traffic analysis that establishes upper and lower bounds on the freshness of system information.
- Distributed optimization has important applications in the practical implementation of machine learning and signal processing setup by providing means to allow interconnected network of processors to work towards the optimization of a global objective with intermittent communication. Existing works on distributed optimization predominantly assume all the processors storing related data to perform updates for the optimization task in each iteration. However, such optimization processes are typically executed at shared computing/data centers along with other concurrent tasks. Therefore, it is necessary to develop efficient distributed optimization methods that possess the flexibility to share the computing resources with other ongoing tasks. In this work, we propose a new first-order framework that allows for this flexibility through a probabilistic computing resource allocation strategy while guaranteeing the satisfactory performance of distributed optimization.
- We consider the optimal link rate selection problem in time-varying wireless channels with unknown channel statistics. The aim of optimal link rate selection is to transmit at the optimal rate at each time slot in order to maximize the expected throughput of the wireless channel/link or equivalently minimize the expected regret. Lack of information about channel state or channel statistics necessitates the use of online/sequential learning algorithms to determine the optimal rate. We present an algorithm called CoTS - Constrained Thompson sampling algorithm which improves upon the current state-of-the-art, is fast and is also general in the sense that it can handle several different constraints in the problem with the same algorithm. We also prove an asymptotic lower bound on the expected regret and a high probability large-horizon upper bound on the regret, which show that the regret grows logarithmically with time in an order sense. We also provide numerical results which establish that CoTS significantly outperforms the current state-of-the-art algorithms.
- The cellular paging (broadcast) protocol strives to the balance between a cellular device's energy consumption and quality-of-service by allowing the device to *only* periodically poll for pending services in its idle, low-power state. For a given cellular device and serving network, the exact time periods when the device polls for services (called the *paging occasion*) are fixed by design in the 4G/5G cellular protocol. In this paper, we show that the fixed nature of paging occasions can be exploited by an adversary in the vicinity of a victim to associate the victim's soft-identity (e.g., phone number, Twitter handle) with its paging occasion, with only a modest cost, through an attack dubbed ToRPEDO. Consequently, ToRPEDO can enable an adversary to verify a victim's coarse-grained location information, inject fabricated paging messages, and mount denial-of-service attacks. We also demonstrate that, in 4G and 5G, it is plausible for an adversary to retrieve a victim device's persistent identity (i.e., IMSI) with a brute-force IMSI-Cracking attack while using ToRPEDO as an attack sub-step. Our further investigation on 4G paging protocol deployments also identified an *implementation oversight* of several network providers which enables the adversary to launch an attack, named PIERCER, for associating a victim's phone number with its IMSI; subsequently allowing targeted user location tracking. All of our attacks have been validated and evaluated in the wild using commodity hardware and software. We finally discuss potential countermeasures against the presented attacks.
- In the cellular ecosystem, base stations act as trusted intermediaries between cellular devices and the core network. During connection bootstrapping, devices currently, however, do not possess any mechanisms to authenticate a base station before connecting to it. This lack of authentication has been shown to be exploitable by adversaries to install fake base stations which can lure unsuspecting devices to connect to them and then launch sophisticated attacks. Despite being a well-known threat to the cellular ecosystem, this weakness is not addressed in the current protocol versions including 5G. The current paper sets out to fill this void by proposing a Public-key infrastructure (PKI) based authentication mechanism which builds on top of the asymmetric cryptography used in 5G and adheres to the relevant deployment constraints.
- Traditionally, a Certification Authority (CA) is required to sign, manage, verify and revoke public key certificates. Multiple CAs together form the CA-based Public Key Infrastructure (PKI). The use of a PKI forces one to place trust in the CAs, which have proven to be a single point of-failure on multiple occasions. Blockchain has emerged as a transformational technology that replaces centralized trusted third parties with a decentralized, publicly verifiable, peer-to-peer data store which maintains data integrity among nodes through various consensus protocols. In this paper, we deploy three blockchain-based alternatives to the CA-based PKI for supporting IoT devices, based on Emercoin Name Value Service (NVS), smart contracts by Ethereum blockchain, and Ethereum Light Sync client. We compare these approaches with CA-based PKI and show that they are much more efficient in terms of computational and storage requirements in addition to providing a more robust and scalable PKI.
- The power of Information-Centric Networking (ICN) architectures lies in their abstraction for communication — the request for named data. This abstraction promises that applications can choose to operate only in the information plane, agnostic to the mechanisms implemented in the connectivity plane. However, despite this powerful promise, the information and connectivity planes are presently coupled in today’s incarnations of leading ICNs by a core architectural component, the forwarding strategy. Presently, this component is not sustainable: it implements both the information and connectivity mechanisms without specifying who should choose a forwarding strategy — an application developer or the network operator. In practice, application developers can specify a strategy only if they understand connectivity details, while network operators can assign strategies only if they understand application expectations. In this paper, we define the role of forwarding strategies, and we introduce Information-Centric Transport (ICT) as an abstraction for cleanly decoupling the information plane from the connectivity plane. We discuss how ICTs allow applications to operate in the information plane, concerned only with namespaces and trust identities, leaving network node operators free to deploy whatever strategy mechanisms make sense for the connectivity that they manage. To illustrate the ICT concept, we demonstrate ICT-Sync and ICT-Notify. We show how these ICTs 1) enable applications to operate regardless of connectivity details, 2) are designed to satisfy a predefined set of application requirements and are free from application-specifics, and 3) can be deployed by network operators where needed, without requiring any change to the application logic.
- Estimating and tracking the head orientation of the user is an important problem for numerous mobile computing applications. Current solutions to the problem require deploying infrastructure (namely, cameras and lasers) along with expensive (IMU) sensors. These infrastructure-based approaches bound the user to a limited area of tracking and also disable portability and mobility of the user. This work presents HeadTrack and explores the feasibility of designing a necklace-like wearable consisting of a headset and a chest-piece that can be used to estimate the user’s head orientation using wireless (radio frequency) signals. The core problem presented in this thesis is to accurately estimate multiple distances between the chest-piece on the torso and the headset using the ultra-wide band (UWB) radios. Such a wearable not only enables portability but also mobility by decoupling user’s head motion from the body motion. Although the ultra-wide band radios have a 1GHz bandwidth and high-speed clocks, they are unable to do sub-centimeter ranging. We improve the typical ∼10cm accuracy of the UWB radios by introducing a wired path between the transmitters and the receivers to serve as a reference point. We split the signal at the transmitter and route it through the wired as well as the wireless paths to improve the accuracy to about 5mm. We use ViCon to collect the ground truth for our experiments and evaluate our system. HeadTrack uses an IMU to resolve the phase wrap ambiguities and is able to track the head orientation of the user with an accuracy of 6.5 ◦ . HeadTrack provides a wearable, occlusion free, portable, and cost-effective solution to the problem of head orientation tracking with a bounded and non-diverging error.
- A rich body of work has focused on motion tracking techniques using inertial sensors, namely accelerometers, gyroscopes, and magnetometers. Applications of these techniques are in indoor localization, gesture recognition, inventory tracking, vehicular motion, and many others. This paper identifies room for improvement over today’s motion tracking techniques. The core observation is that conventional systems have trusted gravity more than the magnetic North to infer the 3D orientation of the object. We find that the reverse is more effective, especially when the object is in continuous fast motion. We leverage this opportunity to design MUSE, a magnetometer-centric sensor fusion algorithm for orientation tracking. Moreover, when the object’s motion is somewhat restricted (e.g., human-arm motion restricted by elbow and shoulder joints), we find new methods of sensor fusion to fully leverage the restrictions. Real experiments across a wide range of uncontrolled scenarios show consistent improvement in orientation and location accuracy, without requiring any training or machine learning. We believe this is an important progress in the otherwise mature field of IMU-based motion tracking.
- Using PULS, we implemented four different scheduling policies and provide detailed performance comparisons under various traffic loads and real-time requirements. We show that in certain scenarios, the optimal policy can maintain a loss ratio of less than 1% for packets with deadlines, while other protocols experience loss ratios of up to 65%.
- Informed by the results of our analysis, we propose a novel hybrid algorithm, Adaptive-LRU (A-LRU), that learns both faster and better the changes in the popularity. We show numerically that it also outperforms all other candidate algorithms when confronted with either a dynamically changing synthetic request process or using real world traces.
- Motivated by mm-wave networking, we have obtained scheduling policies for maximizing the throughput of packets that satisfy hard deadline bounds in multi-hop wireless networks with lossy links. We have determined optimal policies that are both tractably computed and can be implemented in a distributed way at the nodes in the network when they are subject to average power constraints.
- In the mm-wave band, transmissions are directional and lossy. Therefore base stations will need to track mobile users in order to provide connectivity. We have developed such a MAC for mm-wave base stations and their clients.
- In sensor-actuator networks, providing timely refresh of sensor measurements is important for control loop performance. We have developed a policy for inter-delivery time optimization.
- We demonstrate that our policies for QoE optimization achieve the optimal overall utility when their parameters are chosen properly. Finally, we compare our policies against three popular policies. Simulation and experimental results validate that the proposed policies indeed outperform existing policies.
- The utility of our online policy for service caching at the wireless edge, RED/LED, is evaluated on real-world traces. These trace-based simulations demonstrate that our policy has better, or similar, performance compared to many intelligent offline policies.
- The effectiveness of our mechanism for non-monetary efficient cost allocation is extensively evaluated via simulations of both delay allocation and loss rate allocation against baseline mechanisms with classic control policies.
- In online job allocation, we propose policies with low complexity. For systems with persistent jobs, we prove that our policies can achieve a given hard allocation ratio requirement with the least capacity. For systems with dynamic jobs, the capacity needed for our policies to achieve the hard allocation ratio requirement is close to a theoretical lower bound. Two other popular policies are studied, and we demonstrate that they need at least an order higher capacity to meet the same hard allocation ratio requirement. Simulation results demonstrate that our policies remain far superior than the other two even when jobs arrive according to some random process.
- For the problem of minimizing LoC, we show that it requires the control of temporal variance of timely deliveries for each flow. This feature makes this problem significantly different from other optimization problems that only involves the average of control variables. Surprisingly, we show that there exists a simple online scheduling algorithm that is near-optimal. Simulation results show that our proposed algorithm is significantly better than other state-of-the-art policies.
- In real-time wireless ad hoc networks, we prove that our proposed decentralized algorithm is feasibility-optimal. NS-3 simulation results show that the proposed algorithm indeed performs as well as the feasibility-optimal centralized algorithm. Moreover, the results also show that the proposed algorithm converges to optimality very fast.
- This work provides the fundamental limits, as well as the guidelines, for the design and performance analysis of efficient multicasting strategies for wireless content distribution.
- This work provides new and even counterintuitive insights that motivate novel designs for caching systems over unreliable channels. They can potentially be exploited to further improve the system performance in practice.
- The key message that we learn from this work is the value of interarrival times in maintaining fresh and equally delayed information updates of continuous flows. This insight is expected to be useful in designing the communication backbone of future cyber-physical systems whose operation is critically dependent on the freshness of information.
- Our results in this work, both analytical and numerical, show that by controlling a flexibility parameter, our suite of algorithms (designed for various scenarios) can achieve the lower computation and communication costs of distributed optimization than their inflexible counterparts. This framework also enables the fair sharing of the common resources with other concurrent tasks being processed by the processing network.
- This work reveals the value of Thompson-Sampling over index-based methods for optimizing performance in rapidly changing environments. As such it can be of value in optimizing performance in wireless fading conditions under high mobility and/or fragile mmW communication channels.
- ToRPEDO can enable an adversary to verify a victim's coarse-grained location information, inject fabricated paging messages, and mount denial-of-service attacks. We demonstrate that, in 4G and 5G, it is plausible for an adversary to retrieve a victim device's persistent identity (i.e., IMSI) with a brute-force IMSI-Cracking attack while using ToRPEDO as an attack sub-step. We also discuss potential countermeasures against the presented attacks.
- Our proposed authentication scheme leverages precomputation-based digital signature generation algorithms and employs optimizations in three dimensions—PKI scheme-level, protocol-level, and cryptographic scheme-level—to address the trilemma of small signature size, efficient signature generation, and short verification time. Our evaluation on a real testbed indicates that the proposed scheme is not only readily deployable but also performs better than a symmetric keybased scheme (i.e., TESLA) in terms of security guarantee, overhead,and deployment constraints (e.g., backward compatibility).
- We deploy three blockchain-based alternatives to the CA-based PKI for supporting IoT devices, based on Emercoin Name Value Service (NVS), smart contracts by Ethereum blockchain, and Ethereum Light Sync client. We compare these approaches with CA-based PKI and show that they are much more efficient in terms of computational and storage requirements in addition to providing a more robust and scalable PKI.
- To illustrate the ICT concept, we demonstrate ICT-Sync and ICT-Notify. We show how these ICTs 1) enable applications to operate regardless of connectivity details, 2) are designed to satisfy a predefined set of application requirements and are free from application-specifics, and 3) can be deployed by network operators where needed, without requiring any change to the application logic.
- Tracking head orientation is a hard problem  and the current solutions make it an expensive one as well. While most current solutions use very expensive hardware (lasers, cameras, and expensive IMUs), we explore the possibility to develop a system that not only 36 enables portability but can also be significantly cheaper than the current solutions. We demonstrate that it is feasible to make a wearable that can use RF signals to estimate the head orientation of the user independently of the user’s body motion. HeadTrack is able to decouple the head motion and body motion because it uses the transmitter on the torso as an “anchor”. Our results show a median error of 6.5 ◦ which may not be good enough for a few applications that require very high accuracy (like AR/VR systems) but will suffice for most head orientation tracking applications. We believe, HeadTrack provides a wearable, occlusionfree, portable, and cost-effective solution to the problem of head orientation tracking with a bounded and non-diverging error
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