PRISM :: Browsing by Author "Ghaderi, Majid"

Browsing by Author "Ghaderi, Majid"

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Open Access
A Net Present Cost Minimization Framework for Wireless Sensor Networks
(2016) Dorling, Kevin; Messier, Geoffrey; Magierowski, Sebastian; Messier, Geoffrey; Magierowski, Sebastian; Karl, Holger; Ghaderi, Majid; Sesay, Abu-Bakarr; Behjat, Laleh
Minimizing the cost of deploying and operating a wireless sensor network (WSN) involves deciding how to partition a budget between competing expenses such as node hardware, energy, and labour. To determine if funds are given to a specific project or invested elsewhere, companies often use interest rates to sum the project's cash flows in terms of present-day dollars. This provides an incentive to defer expenditures when possible and use the returns to reduce future costs. In this thesis, a framework is proposed for minimizing the net present cost (NPC) of a WSN by optimizing the number of, cost of, and time between expenditures. The proposed framework balances competing expenses and defers expenditures when possible. A similar strategy does not appear to be available in the literature, and has likely not been developed in industry as no commercial WSN operators currently exist. In general, NPC minimization is a non-linear, non-convex optimization problem. However, if the time until the next expenditure is linearly proportional to the cost of the current expenditure, and the number of maintenance cycles is known in advance, the problem becomes convex and can be solved to global optimality. If non-deferrable recurring costs are low, then evenly spacing the expenditures can provide near-optimal results. The NPC minimization framework is most effective when non-deferrable recurring costs, such as labour, are low. High labour costs limit the number of times that a WSN operator can use the returns from investing deferrable costs to decrease future expenditures. This thesis therefore proposes vehicle routing problems (VRPs) to reduce labour costs by delivering nodes with drones. Unlike similar VRPs, drone costs are reduced by reusing vehicles, and low-cost, feasible routes are ensured by modelling energy consumption as a function of drone battery and payload weight. The problems are modelled as mixed integer linear programs (MILPs). As these MILPs are NP-hard, simulated annealing algorithms are proposed for finding sub-optimal solutions to large instances of the problems.
Open Access
Adaptive Model Aggregation for Decentralized Federated Learning in Vehicular Networks
(2023-12-20) Movahedian Attar, Mahtab; Ghaderi, Majid; Ovens, Katie; Drew, Steve
In vehicular networks, Decentralized Federated Learning (DFL) has emerged as a cutting-edge methodology for the collaborative development of machine learning models, offering a unique balance between collaborative learning and individual data privacy. This approach is particularly apt in scenarios where vehicles, equipped with their own data and computation capabilities, work together to build a comprehensive and shared global model. Each vehicle contributes to this goal by training local models and merging them through model aggregation with data from nearby vehicles. A notable challenge in this context arises from vehicular networks' characteristics - primarily, their high mobility and the often unreliable nature of wireless communications. These factors frequently result in vehicles receiving only fragments of models from their peers, presenting a complex challenge: how to effectively leverage these incomplete models to accelerate the overall training process while simultaneously managing to keep the computational and communication overhead to a minimum. This thesis introduces the Adaptive Model Aggregation (AMA) algorithm to meet this challenge. AMA functions asynchronously within each vehicle and utilizes a threshold-based mechanism to decide whether to include the partially received models in the aggregation process. Given the variable nature of vehicular networks, it becomes evident that a fixed threshold is insufficient. Consequently, this thesis innovates an algorithm inspired by Contextual Multi-Arm Bandit (CMAB) theory, adapting the threshold for each vehicle based on the current communication dynamics of the network. The efficacy of AMA is tested through a series of comprehensive evaluations conducted within simulated environments, which account for various vehicular mobility patterns. These evaluations provide robust evidence that AMA substantially reduces the overhead associated with DFL aggregation - by as much as 83% - compared to traditional, non-adaptive aggregation methods, without adversely affecting the accuracy of the training process.
Open Access
Bandit-based Delay-Aware Service Function Chain Orchestration at the Edge
(2021-04-21) Wang, Lei; Ghaderi, Majid; Krishnamurthy, Diwakar; Safavi-Naini, Rei
Mobile Edge Computing (MEC) enables both cloud computing and edge computing for mobile users, providing them with intensive computing resources and proximity to the data sources. When combined with network function virtualization (NFV), MEC provides users with promising end-to-end latency and management for mobile applications that requires multiple computing resources. Such applications are often handled in a fashion of service function chain (SFC), which designates a sequence of virtual network functions (VNF) for users’ traffic to traverse in order to realize their network application. In order to provide the user a tolerated perceived latency for a SFC-based application, many existing works have taken aim at optimal system-wide placement for SFC in heterogeneous scenarios yet fewer works have studied user-managed placement. In this paper, we formulate the user-managed SFC placement in MEC as a contextual combinatorial multi-arm bandit (C2MAB) problem and proposed BandEdge, a bandit-based algorithm for online SFC placement on edge, which consider user’s mobility and service preference while jointly optimizing their perceived latency and service migration delay, and then propose an offline exact approach for the role of performance benchmark. To fit the SFC placement problem in a bandit framework, we model the nodes and links to be arms by viewing them as delays and selects them according to a strategy that balances exploration and exploitation. Finally, we evaluate the proposed algorithm in extensive simulation and Mininet-WiFi emulation experiments, numeric simulation results show that the proposed algorithm can achieve close-to-optimum performance and outperform the greedy learning algorithms by at least 50 percent in terms of scalability. We further validate the superior performance of our proposed method in Mininet-WiFi emulation under different environmental parameters.
Open Access
A Campus-Level View of DNS Trafﬁc
(2019-07-26) Zhang, Zhengping; Williamson, Carey L.; Arlitt, Martin F.; Williamson, Carey L.; Arlitt, Martin F.; Ghaderi, Majid; Aycock, John
This thesis presents a characterization study of DNS trafﬁc within the University of Calgary edge network. The trafﬁc analysis is based on a one-week period of observation (from September 3, 2018 to September 9, 2018). We study the two directions (outbound and inbound) of the DNS trafﬁc, representing the two roles that the campus plays in the DNS architecture, namely a user and a service provider. We selectively analyzed the trafﬁc of a few campus DNS servers. In addition, we also examine several DNS-related anomalies. The measurement results show that a signiﬁcant proportion of DNS messages come from misconﬁgurations or answers with short TTLs, which can both be improved to reduce the DNS trafﬁc volume.
Open Access
Congestion Control in Software-Defined Networks: A Simulation Study
(2019-11) Gholizadeh, Reza; Williamson, Carey L.; Ghaderi, Majid; Costa Sousa, Mario
Congestion is an underlying reason for performance degradation in computer networks. Current TCP congestion control has no information about the network. Hence, it increases the sending window to overflow the bottleneck link buffer, and backs off when packet drops are detected. Software-Defined Networking SDN is a new paradigm, which provides information about the network. In this thesis, we propose a novel centralized congestion control scheme for SDN. Our solution exploits the information provided by the SDN controller to prevent formation of persistent queues in bottleneck links. Also, we introduce an SDN Simulation Tool developed in Java, which facilitates simulation experiments. We used our tool to evaluate the proposed solution. The preliminary results shows the potential scalability and flexibility of the protocol.
Open Access
Contextual Anomaly Detection in Controller Area Networks
(2022-03) Balaji, Prashanth; Ghaderi, Majid; Hudson, Jonathan; Henry, Ryan
The Controller Area Network (CAN) has been an established standard for in-vehicular networks for over two decades. The low implementation cost of CAN together with its simple design has allowed automotive manufacturers to incorporate it at scale with ease. The onboard CAN bus facilitates real-time data exchange between Electronic Control Units (ECU) that are responsible for maintaining critical functions such as lane-keep assist, collision assist and engine control during the operation of the vehicle. Though proven to be reliable and efficient, security was never a part of CAN's design. Hence ECUs are highly susceptible to a wide range of attacks that could eventually prove fatal to passengers and all road users. Additionally, the increased connectivity in Connected and Autonomous Vehicles (CAV) has further widened the threat landscape for malicious actors to leverage. Attackers typically target specific vehicle subsytems by injecting malicious exploits into the bus and thus anomaly detection in the CAN has been actively studied in recent years. While existing detection systems are capable of identifying deviations in the behavior of an individual control unit, they are ineffective against attacks that target multiple subsystems while still adhering to the norms of the system. Such stealthy attacks are more subjective to evade the purview of an anomaly detection system that does not collectively evaluate all data points to determine the overall state of the system. In this thesis, we primarily focus on detecting these attacks by identifying contextual anomalies in CAN bus data. To this end, we employ machine learning algorithms to capture the spatio-temporal correlations among sensor readings in the CAN bus at both frame and signal levels. Neural networks are typically capable of learning intrinsic patterns in the given data without the need to comprehend its meaning and thus this use case provides an ideal ground for their application. We present NeuroCAN, a deep learning-based detection model that employs Long Short-Term Memory (LSTM) and Linear Embeddings to derive contextual inferences from other ECUs in real-time. We train and evaluate our approach on two real-world CAN bus datasets and compare its performance against other existing approaches in the literature. Following which we assess the capacity of our model to identify stealthy attacks in an open-source signal dataset that serves as a benchmark for CAN bus anomaly detection systems. The results indicate that our system is capable of achieving over 95% detection accuracy and performs significantly better than other state of the art approaches. We further incorporate multitask learning to effectively reduce the large resource overhead that arises over managing multiple trained models during detection. We also study the importance of additional sensor context and the need for a collective approach in the detection process and present our findings.
Open Access
Contributions to Information Theoretic Multiterminal Secret Key Agreement
(2022-01) Poostindouz, Alireza; Safavi-Naeini, Reyhaneh; Ghaderi, Majid; Gour, Gilad; Fapojuwo, Abraham O.; Sprintson, Alex
A multiterminal secret key agreement (SKA) protocol is used to establish a shared se- cret key among a group of terminals. We study SKA protocols with information-theoretic security. In the source model of SKA, each terminal can sample from a correlated random variable. In the channel model of SKA, terminals instead are connected through an un- derlying noisy channel that is used for distributing the correlated variables. The terminals arrive at a shared secret key by establishing correlation (as per the presumed source/channel model) and communicating over a noiseless authenticated public channel. In the general models of SKA, it is assumed that terminals’ variables are partially leaked to the adversary, Eve, in the form of a random variable which we call Eve’s wiretap side information. Eve has unlimited computational power and has read access to all public communication mes- sages. The key rate of an SKA protocol is given by the key length divided by the terminals’ variables length, and the maximum possible key rate calculated for an SKA model is called the wiretap secret key (WSK) capacity of that model. Finding a general expression for the WSK capacity continues to be one of the hardest open problems within the context of information-theoretic key agreement. Our contributions include proving the WSK capacity and proposing capacity achieving SKA protocols for the wiretapped PIN, Tree-PIN, and Polytree-PIN models, that are special multiterminal SKA models of interest in practice. Also, we introduce a new channel model of SKA that we call the transceiver model for which we prove multiple upper and lower bounds on key capacity under various assumptions. Furthermore, we note that traditionally the key capacity was studied and calculated for SKA models, while in the actual implementation of SKA protocols, the achievable key length as a function of terminals’ variables length is needed. Compared to calculating WSK capacity, finding the key length requires different information-theoretic techniques for evaluating the protocols. We prove finite-length upper and lower bounds on the maximum achievable key length for some of the models that we have considered. In the concluding sections, we outline directions for future research.
Open Access
Cooperative Diversity Routing in Wireless Networks
(2009-10-07T16:01:56Z) Dehghan, Mostafa; Ghaderi, Majid; Goeckel, Dennis
In this paper, we explore physical layer cooperative communication in order to design network layer routing algorithms that are energy efficient. We assume each node in the network is equipped with a single omnidirectional antenna and that multiple nodes are able to coordinate their transmissions in order to take advantage of spatial diversity to save energy. Specifically, we consider cooperative diversity at physical layer and multi-hop routing at network layer, and formulate minimum energy routing as a joint optimization of the transmission power at the physical layer and the link selection at the network layer. We then show that as the network becomes larger, finding optimal cooperative routes becomes computationally intractable. As such, we develop a number of heuristic routing algorithms that have polynomial computational complexity, and yet achieve significant energy savings. Simulation results are also presented, which indicate that the proposed algorithms based on optimal power allocation significantly outperform existing algorithms based on equal power allocation, by more than 60% in some simulated scenarios.
Open Access
Coordinated Packet-Level Traffic Monitoring in Software-Defined Networks
(2023-01-19) Sadrhaghighi, Sogand; Ghaderi, Majid; Reardon, Joel; Wang, Mea; Williamson, Carey; Krishnamurthy, Diwakar; Liang, Ben
As the scale and speed of networks grow, packet-level monitoring has become an indispensable tool for extensive network-wide visibility. Traditional tools for capturing packet-level traces have either become unfit or do not meet the requirements of modern networks. This thesis presents the design and evaluation of software-defined packet-level monitoring solutions that address the monitoring requirements of modern high-speed networks. In particular, we present the design and evaluation of SoftTap, a scalable alternative to hardware taps, which provides pervasive flow visibility utilizing the traffic mirroring capabilities of commodity OpenFlow switches. To decide on the mirroring configurations, we design polynomial time approximation algorithms with bounded approximation ratios. Our Mininet experiments show that an intrusion detection system implemented on top of SoftTap achieves up to 25% higher detection recall compared to existing mirroring solutions. To reduce the monitoring overhead, networks adopt traffic sampling solutions. Existing sampling solutions, however, either provide limited flow visibility or scale poorly in large networks. We present the design and evaluation of FlowShark, a high-visibility per-flow sampling system for software-defined networks. The main idea in FlowShark is to manage sampling decisions on short flows using edge switches, whereas a central controller optimizes sampling decisions on long flows. To manage long flow sampling decisions, we design an online algorithm with a bounded competitive ratio. Our Mininet experiments with a machine learning-based traffic classifier show up to 27% higher classification recall with FlowShark compared to existing sampling solutions. Deploying network-wide packet-level monitoring solutions in multi-tenant virtual networks (VNs) remains challenging. Existing solutions, in which each VN configures mirroring or sampling independently of other VNs, lead to inefficiencies. We present the design and evaluation of Open Virtual Tap and SampVisor, network-wide virtualization-aware flow mirroring and sampling monitoring solutions, respectively. The key idea behind both systems is the joint configuration of all switches in the substrate physical network to efficiently mirror/sample flows from all VNs. We formulate virtualization-aware flow mirroring and sampling as optimization problems and design efficient algorithms with bounded worst-case performance to solve the problems.
Open Access
Covert Communication in Autoencoder Wireless Systems
(2023-09-12) Mohammadi, Ali Teshnizi; Ghaderi, Majid; Ovens, Katie; Fapojuwo, Abraham
The broadcast nature of wireless communications presents security and privacy challenges. Covert communication is a wireless security practice that focuses on intentionally hiding transmitted information. Recently, wireless systems have experienced significant growth, including the emergence of autoencoder-based models. These models, like other DNN architectures, are vulnerable to adversarial attacks, highlighting the need to study their susceptibility to covert communication. While there is ample research on covert communication in traditional wireless systems, the investigation of autoencoder wireless systems remains scarce. Furthermore, many existing covert methods are either detectable analytically or difficult to adapt to diverse wireless systems. The first part of this thesis provides a comprehensive examination of autoencoder-based communication systems in various scenarios and channel conditions. It begins with an introduction to autoencoder communication systems, followed by a detailed discussion of our own implementation and evaluation results. This serves as a solid foundation for the subsequent part of the thesis, where we propose a GAN-based covert communication model. By treating the covert sender, covert receiver, and observer as generator, decoder, and discriminator neural networks, respectively, we conduct joint training in an adversarial setting to develop a covert communication scheme that can be integrated into any normal autoencoder. Our proposal minimizes the impact on ongoing normal communication, addressing previous works shortcomings. We also introduce a training algorithm that allows for the desired tradeoff between covertness and reliability. Numerical results demonstrate the establishment of a reliable and undetectable channel between covert users, regardless of the cover signal or channel condition, with minimal disruption to the normal system operation.
Open Access
Deadline-aware Bulk Transfer Scheduling in Best-effort SD-WANs
(2021-04-16) Hosseini Bidi, Seyed Arshia; Ghaderi, Majid; Hudson, Jonathan; Fupojuwo, Abraham
Wide area networks (WANs) that connect geo-distributed datacenters enable online applications to provide a diversity of services to their users in various locations throughout the world. Inter-datacenter (inter-DC) traffic constitutes a significant portion of today’s world-wide traffic while utilizing dedicated lines that are in different networks than the Internet, making it a very expensive communication. Consequently, inter-DC network providers are keen to minimize their expenses while guaranteeing the quality of service to their customers. As a result, scheduling available resources is of paramount importance to increase the efficacy of these networks for both their providers and customers. In this regard, software-defined wide area networks (SD-WAN) seem to be a promising solution to mitigate legacy WAN’s restrictions such as lack of a global view. While conventional multi-protocol label switching (MPLS) tunnelling has proven to be a practical approach to guarantee performance, its significant service price can be a drawback. Utilizing Internet best-effort paths is a cheap and viable alternative. However, to utilize these paths, we have to take their capacity fluctuations into account to avoid over-allocation. In this thesis, we first characterize and estimate the fluctuations in short and long periods using statistical analysis and machine learning. Next, we take the estimated capacities into account and consider the problem of scheduling bulk transfer requests over best-effort SD-WANs to maximize the gained profit from successful transmissions. Furthermore, we propose an approximate algorithm with a significant computational advantage over our exact algorithm with an approximation ratio that only depends on the number of overlapping requests with the same profit to bandwidth ratio. Finally, we provide a thorough mathematical analysis of the approximate algorithm, as well as simulation and experimental results to evaluate our proposed algorithm’s performance. The results show that our algorithm can improve the inter-DC provider’s profit by an average of 60% while reducing ISP service costs by an average of 15%.
Open Access
Deadline-aware Service Function Orchestration under Demand Uncertainty
(2020-01-17) Nguyen, Quang Minh; Ghaderi, Majid; Williamson, Carey L.; Fapojuwo, Abraham O.
In network function virtualization (NFV), a service function chain (SFC) specifies a sequence of virtual network functions (VNFs) that user traffic has to traverse to realize a network service. A service can either be delivered by VNFs co-located within a single network infrastructure or geo-distributed over multiple distant cloud infrastructures. In either scenario, as the network resources are shared among multiple SFCs, optimal allocation of network resources to ensure the required quality of service while minimizing the deployment cost is a key challenge. This problem is commonly referred to as the SFC orchestration problem, which has been studied extensively in various settings. However, most existing works assume deterministic demands and resort to costly runtime resource reprovisioning to deal with dynamic demands. In this work, we formulate the co-located and geo-distributed SFC orchestration with demand uncertainty as robust optimization problems and develop exact and approximate algorithms to solve them. To avoid continuous resource reprovisioning, our algorithms utilize uncertain demand knowledge to compute proactive service orchestration solutions that can cope with fluctuations in dynamic service demands. The uncertain demand is modeled as a constrained uncertainty set whose cardinality can be adjusted to control the algorithm proactivity against demand fluctuations. We present extensive model-driven simulation results to study the behavior of the proposed algorithms in small and large scale problem instances and demonstrate their ability to achieve any desired proactivity-cost trade-off. We also evaluate the performance of our algorithms against other state-of-the-art algorithms in the literature. Mininet experiments are further conducted to validate the modeling of different components in our system model.
Open Access
Design and Analysis of Wireless Networks for Petroleum Refineries
(2017) Herrmann, Michael James; Messier, Geoffrey; Fapojuwo, Abraham Olatunji; Ghaderi, Majid
This thesis investigates wireless sensor network design for industrial petroleum refineries. A wireless channel measurement campaign is conducted in a Shell Canada gas refinery west of Calgary. The propagation measurements are the first to characterize the large-scale channel statistics for peer-to-peer transmission in an outdoor refinery environment. It is shown that the propagation is well characterized by the familiar standard pathloss and log-normal shadowing model. This model is then used in building a machine-to-machine network performance simulation for process control in a petroleum refinery. The simulation requires the use of cross-layer optimizations to determine the routes which maximize the network lifetime. Linear Programming and Mixed Integer Linear Programming problems are formulated and evaluated to solve the cross-layer design problem. Carrier Sense Multiple Access (CSMA) and Time Division Multiple Access (TDMA) schemes are both used, but it is shown that TDMA has superior performance.
Open Access
Distributed Energy Minimization in Heterogeneous Cellular Networks
(2015-11-17) Naghibi, Seyedmohammad; Ghaderi, Majid
Heterogeneous networks are designed to increase the capacity for cellular data traffic. Self-organization is a key element of heterogeneous cellular networks. In this thesis, we present a randomized algorithm that addresses two challenges in HetNets, namely energy saving and throughput maximization, in a self-organizing manner. More specifically, the proposed algorithm seeks to maximize an objective function that balances the trade-off between the downlink bit rate of users, and the energy consumption of base stations. To achieve this goal, we deactivate under-utilized picocells to save energy, and adjust low-power Almost Blank Subframes to utilize the frequency spectrum and minimize the interference between macrocells and picocells. An important feature of our algorithm is its distributed design, which eliminates the need for a central device to coordinate the base stations. In fact, the base stations directly interact with each other in a locally defined neighborhood to drive the system toward the optimal state.
Open Access
Distributed Routing for Vehicular Ad Hoc Networks: Throughput-Delay Tradeoff
(2009-12-16T18:24:05Z) Abedi, Ali; Ghaderi, Majid; Williamson, Carey
In this paper, we address the problem of low-latency routing in a vehicular highway network. To cover long highways while minimizing the number of required roadside access points, we utilize vehicle-to-vehicle communication to propagate data in the network. Vehicular networks are highly dynamic, and hence routing algorithms that require global network state information or centralized coordination are not suitable for such networks. Instead, we develop a novel distributed routing algorithm that requires minimal coordination among vehicles, while achieving a highly efficient throughput-delay tradeoff. Specifically, we show that the proposed algorithm achieves a throughput that is within a factor of 1=e of the throughput of an algorithm that centrally coordinates vehicle transmissions in a highly dense network, and yet its end-to-end delay is approximately half of that of a widely studied ALOHA-based randomized routing algorithm. We evaluate our algorithm analytically and through simulations and compare its throughput-delay performance against the ALOHA-based randomized routing.
Open Access
Driving Anomaly Detection Using Recurrent Neural Networks
(2022-03-25) Sabour, Sepehr; Ghaderi, Majid; Stefanakis, Emmanuel; Hudson, Jonathan William
Deep learning has changed many aspects of our lives in recent years. Every day, the improvements in artificial intelligence make computers more capable of doing our daily tasks. Traffic management has never been separated from these changes. Researchers have proposed many machine learning solutions to help traffic management centers monitor vehicles’ activities in the transportation networks. Driving anomaly detection refers to finding unexpected vehicles, situations and traffic flows in the transportation systems. Many research works have been conducted recently to address driving anomaly detection problem, however each of these solutions has drawbacks. This thesis suggests two innovative solutions for detecting anomalies in intelligent transportation systems using recurrent neural networks (RNNs). A brief introduction of driving anomaly detection techniques and RNNs is presented in the first part of the thesis. Then in the second part, two suggested solutions, DeepFlow and ThirdEye, are discussed. DeepFlow is a method to detect abnormal traffic flows in smart cities. It is argued in this thesis that finding a complete dataset of vehicles’ behaviors in driving scenarios is very difficult. To address this issue the DeepFlow solution from this thesis applies machine learning techniques to reduce the requirement for a comprehensive dataset without loosing accuracy. ThirdEye, the second solution introduced, focuses on detecting anomalous behaviors of driver-less vehicles. This model works based on predicting the vehicle’s state in the future. By measuring the distance between the actual state of the vehicle and the predicted one, the system can detect more than 90% of anomalies. Three different recurrent neural networks were tested to determine the best for ThirdEye.
Open Access
DTLS with Post Quantum Security for Origin Authentication and Integrity
(2020-09-24) Parveen, Simpy; Safavi-Naini, Reihaneh S.; Ghaderi, Majid; Yanushkevich, Svetlana N.; Safavi-Naini, Reihaneh S.
All public-key cryptography algorithms that are in use today, including RSA (Rivest–Shamir- Adleman) cryptosystem, DSA (Digital Signature Algorithm), and DH (Diffie-Hellman) key agreement, will be broken if quantum computers become a reality. Hence, applications and protocols must be transitioned to quantum-resistant designs. We consider post-quantum security of DTLS (Datagram Transport Layer Security) for source authentication and message integrity. These are essential security requirements for control plane communications in 5G networks. To provide message integrity while avoiding costly post-quantum secure key exchange protocols that rely on unproven computational assumptions, we will use TESLA (Timed Efficient Stream Loss-tolerant Authentication) protocol. TESLA is a data stream authentication protocol that uses symmetric-key cryptographic primitives and a digital signature scheme to achieve security. We first replace the digital signature in TESLA with a hash-based one to achieve post-quantum security, and then carefully revise the DTLS handshake and record layer protocol to include the new TESLA protocol such that it delivers the same properties for DTLS. We argue our design’s security and show our model’s feasibility using an efficient implementation for an open-source DTLS library, called TinyDTLS. Finally, we provide performance measurements for PQ-DTLS compared with original DTLS in authentication and integrity only mode.
Open Access
Energy Efficient Cooperative Routing in Wireless Networks
(2009-06-05T19:45:20Z) Dehghan, Mostafa; Ghaderi, Majid
In this paper, we explore physical layer cooperative communication in order to design network layer routing algorithms that are energy efficient. We assume each node in the network is equipped with a single omnidirectional antenna and that multiple nodes are able to coordinate their transmissions in order to take advantage of spatial diversity to save energy. Specifically, we consider cooperative MIMO at physical layer and multi-hop routing at network layer, and formulate minimum energy routing as a joint optimization of the transmission power at the physical layer and the link selection at the network layer. Using dynamic programming, we compute the energy consumption of the optimal cooperative routing in different network scenarios, which shows energy savings of up to 55%, compared with the optimal non-cooperative routing. As the network becomes larger, however, finding optimal routes becomes computationally intractable as the complexity of the dynamic programming approach increases as O(22n), where n is the number of nodes in the network. As such, we develop two greedy routing algorithms that have complexity of O(n2), and yet achieve significant energy savings. Simulation results indicate that the proposed greedy algorithms perform almost as good as the optimal algorithm and achieve energy savings of more than 50% in the simulated scenarios.
Open Access
Energy-Efficient Cooperative Routing in Wireless Networks
(2010) Dehghan Shirehpaz, Mostafa; Ghaderi, Majid; Wang, Mea; Fapojuwo, Abraham Olatunji
In this thesis, we explore physical layer cooperative communication in order to design network layer routing algorithms that are energy efficient. We assume each node in the network is equipped with a single omnidirectional antenna and that multiple nodes are able to coordinate their transmissions in order to take advantage of spatial diversity to save energy. Specifically, we consider cooperative diversity at physical layer and multi-hop routing at network layer, and formulate minimum energy routing as a joint optimization of the transmission power at the physical layer and the link selection at the network layer. We study wireless networks with both static and time-varying channels. Based on the optimal algorithms described throughout the thesis, we develop heuristic cooperative routing algorithms that find suboptimal routes, which are computationally simpler. Simulation results are also presented, which investigate the performance of optimal and heuristic routing algorithms in terms of energy efficiency and throughput.
Open Access
Energy-Efficient Workload Placement with Bounded Slowdown in Disaggregated Datacenters
(2023-12-08) Sefati, Amirhossein; Ghaderi, Majid; Krishnamurthy, Diwakar; Boyd, Jeffrey; Ghaderi, Majid
Disaggregated Data Center (DDC) is a modern datacenter architecture that decouples hardware resources from monolithic servers into pools of resources that can be dynamically composed to match diverse workload requirements. While disaggregation improves resource utilization, it could negatively impact workload slowdown due to the latency of accessing disaggregated resources over the datacenter network. To this end, we consider CPU and memory disaggregation and conduct measurements to experimentally profile several popular datacenter workloads in order to characterize the impact of disaggregation on workload execution slowdown. We then develop a workload placement algorithm, called Iterative Rounding-based Placement ( IRoP), that given a set of workloads, determines where to place each workload (i.e., on which CPU) and how much local and remote memory is allocated to it. The key insight in designing IRoP is that the impact of remote memory latency on slowdown can be substantially masked by assigning workloads to higher-performing CPUs, albeit at the cost of higher power consumption. As such, IRoP aims to find a workload placement that minimizes the DDC power consumption while respecting a bounded slowdown for each workload. We provide extensive simulation results to demonstrate the flexibility of IRoP in providing a wide range of trade-offs between power consumption and workload slowdown. We also compare IRoP with several existing baselines. Our results indicate that IRoP can reduce power consumption and slowdown in the considered scenarios by up to 8% and 12%, respectively.