Motivated by emerging vision-based intelligent services, we consider the problem of rate adaptation for high-quality and low-delay visual information delivery over wireless networks using scalable video coding. Rate adaptation in this setting is inherently challenging due to the interplay between the variability of the wireless channels, the queuing at the network nodes, and the frame-based decoding and playback of the video content at the receiver at very short time scales. To address the problem, we propose a low-complexity model-based rate adaptation algorithm for scalable video streaming systems, building on a novel performance model based on stochastic network calculus. We validate the analytic model using extensive simulations. We show that it allows fast near-optimal rate adaptation for fixed transmission paths, as well as cross-layer optimized routing and video rate adaptation in mesh networks, with less than 10% quality degradation compared to the best achievable performance.

In-network caching is an appealing solution to cope with the increasing bandwidth demand of video, audio and data transfer over the Internet. Nonetheless, an increasing share of content delivery services adopt encryption through HTTPS, which is not compatible with traditional ISP-managed approaches like transparent and proxy caching. This raises the need for solutions involving both Internet Service Providers (ISP) and Content Providers (CP): by design, the solution should preserve business-critical CP information (e.g., content popularity, user preferences) on the one hand, while allowing for a deeper integration of caches in the ISP architecture (e.g., in 5G femto-cells) on the other hand. In this paper we address this issue by considering a content-oblivious ISP-operated cache. The ISP allocates the cache storage to various content providers so as to maximize the bandwidth savings provided by the cache: the main novelty lies in the fact that, to protect business-critical information, ISPs only need to measure the aggregated miss rates of the individual CPs and do not need to be aware of the objects that are requested, as in classic caching. We propose a cache allocation algorithm based on a perturbed stochastic subgradient method, and prove that the algorithm converges close to the allocation that maximizes the overall cache hit rate. We use extensive simulations to validate the algorithm and to assess its convergence rate under stationary and non-stationary content popularity. Our results (i) testify the feasibility of content-oblivious caches and (ii) show that the proposed algorithm can achieve within 10% from the global optimum in our evaluation.

Automated and smart meters are devices that are able to monitor the energy consumption of electricity consumers in near real-time. They are considered key technological enablers of the smart grid, as the real-time consumption data that they can collect could enable new sophisticated billing schemes, could facilitate more efficient power distribution system operation and could give rise to a variety of value-added services. At the same time, the energy consumption data that the meters collect are sensitive consumer information; thus, privacy is a key concern and is a major inhibitor of real-time data collection in practice. In this paper, we review the different uses of metering data in the smart grid and the related privacy legislation. We then provide a structured overview, shortcomings, recommendations, and research directions of security solutions that are needed for privacy-preserving meter data delivery and management. We finally survey recent work on privacy-preserving technologies for meter data collection for the three application areas: 1) billing; 2) operations; and 3) value-added services including demand response.

The Internet of Things (IoT) is the next generation of networked society where billions of, everyday-life, devices are directly connected to the Internet and able to communicate with each other. In particular, the Constrained Application Protocol (CoAP) has become the de-facto IoT standard for communication at the application layer, as a lightweight web transfer protocol affordable also for resource-constrained platforms. However, as IoT devices are directly connected to the Internet, they are especially vulnerable to a number of security attacks including Denial of Service (DoS), which can seriously worsen their performance and responsiveness, and even make them totally unavailable to serve legitimate requests.

In this Master's Thesis project, we have developed a cross-layer and context-aware approach that adaptively counteracts DoS attacks against CoAP server devices, by dynamically adjusting their operative state according to the attack intensity. This considerably limits the impact of DoS attacks and preserves service availability of victim devices to the best possible extent. The proposed approach leverages a trusted Proxy that adaptively shields victim devices, while effectively forwarding and caching messages if needed. We have made a proof-of-concept implementation of our solution for the Californium framework and the CoAP protocol, and experimentally evaluated its effectiveness in counteracting DoS and preserving availability of devices under attack.

This Master's Thesis project has been conducted in collaboration with RISE SICS, a research institute for applied information and communication technology in Sweden.

Future cellular networks, the so-called 5G, are expected to provide explosive data volumes and data rates. To meet such a demand, the research communities are investigating new wireless transmission technologies. One of the most promising candidates is in-band full-duplex communications. These communications are characterized by that a wireless device can simultaneously transmit and receive on the same frequency channel. In-band full-duplex communications have the potential to double the spectral efficiencywhen compared to current half duplex systems. The traditional drawback of full-duplex was the interference that leaks from the own transmitter to its own receiver, the so- called self-interference, which renders the receiving signal unsuitable for communication.However, recent advances in self-interference suppression techniques have provided high cancellation and reduced the self-interference to noise floor levels, which shows full-duplex is becoming a realistic technology component of advanced wireless systems.

Although in-band full-duplex promises to double the data rate of existing wireless technologies, its deployment in cellular networks is challenging due to the large number of legacy devices working in half-duplex. A viable introduction in cellular networks is offered by three-node full-duplex deployments, in which only the base stations are full-duplex, whereas the user- or end-devices remain half-duplex. However, in addition to the inherent self-interference, now the interference between users, the user-to-user interference, may become the performance bottleneck, especially as the capability to suppress self-interference improves. Due to this new interference situation, user pairing and frequency channel assignment become of paramount importance, because both mechanisms can help to mitigate the user-to-user interference. It is essential to understand the trade-offs in the performance of full-duplex cellular networks, specially three-node full-duplex, in the design of spectral and energy efficient as well as fair mechanisms.

This thesis investigates the design of spectral efficient and fair mechanisms to improve the performance of full-duplex in cellular networks. The novel analysis proposed in this thesis suggests centralized and distributed user pairing, frequency channel assignment and power allocation solutions to maximize the spectral efficiency and fairness in future full-duplex cellular networks. The investigations are based on distributed optimization theory with mixed integer-real variables and novel extensions of Fast-Lipschitz optimization. The analysis sheds lights on two fundamental problems of standard cellular networks, namely the spectral efficiency and fairness maximization, but in the new context of full-duplex communications. The results in this thesis provide important understanding in the role of user pairing, frequency assignment and power allocation, and reveal the special behaviourbetween the legacy self-interference and the new user-to-user interference. This thesis can provide input to the standardization process of full-duplex communications, and have the potential to be used in the implementation of future full-duplex in cellular networks.

In cellular networks, the three-node full-duplex transmission mode has the po-tential to increase spectral efficiency without requiring full-duplex capability ofusers. Consequently, three-node full-duplex in cellular networks must deal with self-interference and user-to-user interference, which can be managed by power controland user-frequency assignment techniques. This paper investigates the problem ofmaximizing the sum spectral efficiency by jointly determining the transmit powersin a distributed fashion, and assigning users to frequency channels. The problem is for-mulated as a mixed-integer nonlinear problem, which is shown to be non-deterministicpolynomial-time hard. We investigate a close-to-optimal solution approach by dividingthe joint problem into a power control problem and an assignment problem. The powercontrol problem is solved by Fast-Lipschitz optimization, while a greedy solution withguaranteed performance is developed for the assignment problem. Numerical resultsindicate that compared with the half-duplex mode, both spectral and energy efficienciesof the system are increased by the proposed algorithm. Moreover, results show that thepower control and assignment solutions have important, but opposite roles in scenarioswith low or high self-interference cancellation. When the self-interference cancellationis high, user-frequency assignment is more important than power control, while powercontrol is essential at low self-interference cancellation.

To increase the spectral efficiency of wireless networks without requiring full-duplex capability of user devices, a potential solution is the recently proposed three-node full-duplex mode. To realize this potential, networks employing three-node full-duplex transmissions must deal with self-interference and user-to-user interference, which can be managed by frequency channel and power allocation techniques. Whereas previous works investigated either spectral efficient or fair mechanisms, a scheme that balances these two metrics among users is investigated in this paper. This balancing scheme is based on a new solution method of the multi-objective optimization problem to maximize the weighted sum of the per-user spectral efficiency and the minimum spectral efficiency among users. The mixed integer non-linear nature of this problem is dealt by Lagrangian duality. Based on the proposed solution approach, a low-complexity centralized algorithm is developed, which relies on large scale fading measurements that can be advantageously implemented at the base station. Numerical results indicate that the proposed algorithm increases the spectral efficiency and fairness among users without the need of weighting the spectral efficiency. An important conclusion is that managing user-to-user interference by resource assignment and power control is crucial for ensuring spectral efficient and fair operation of full-duplex networks.

We study the transmission of confidential messages across a wireless broadcast channel with K > 2 receivers and K helpers. The goal is to transmit all messages reliably to their intended receivers while keeping them confidential from the unintended receivers. We design a codebook based on nested lattice structure, cooperative jamming, lattice alignment, and i.i.d. coding. Moreover, we exploit the asymmetric compute-and-forward decoding strategy to handle finite SNR regimes. Unlike previous alignment schemes, our achievable rates are attainable at any finite SNR value. Also, we show that our scheme achieves the optimal sum secure degrees of freedom of 1 for the K-receiver Gaussian broadcast channel with K confidential messages and K helpers.

Smart-grid applications based on synchrophasor measurements have recently been shown to be vulnerable to timing attacks. A fundamental question is whether timing attacks could remain undetected by bad-data detection algorithms used in conjunction with state-of-the-art situational-awareness state estimators. In this paper, we analyze the detectability of timing attacks on linear state-estimation. We show that it is possible to forge delay attacks that are undetectable. We give a closed form for an undetectable attack; it imposes two phase offsets to two or more synchrophasor-based measurement units that can be translated to synchrophasors’ time delays. We also propose different methods for combining two-delays attacks to produce a larger impact. We simulate the attacks on a benchmark powertransmission grid, we show that they are successful and can lead to physical grid damage. To prove undetectability, we use classic bad-data detection techniques such as the largest normalized residual and the 2-test.

In electrical distribution grids, the constantly increasing number of power generation devices based on renewables demands a transition from a centralized to a distributed generation paradigm. In fact, power injection from distributed energy resources (DERs) can be selectively controlled to achieve other objectives beyond supporting loads, such as the minimization of the power losses along the distribution lines and the subsequent increase of the grid hosting capacity. However, these technical achievements are only possible if alongside electrical optimization schemes, a suitable market model is set up to promote cooperation from the end users. In contrast with the existing literature, where energy trading and electrical optimization of the grid are often treated separately, or the trading strategy is tailored to a specific electrical optimization objective, in this paper, we consider their joint optimization. We also allow for a modular approach, where the market model can support any smart grid optimization goal. Specifically, we present a multi-objective optimization problem accounting for energy trading, where: 1) DERs try to maximize their profit, resulting from selling their surplus energy; 2) the loads try to minimize their expense; and 3) the main power supplier aims at maximizing the electrical grid efficiency through a suitable discount policy. This optimization problem is proved to be non-convex, and an equivalent convex formulation is derived. Centralized solutions are discussed and a procedure to distribute the solution is proposed. Numerical results to demonstrate the effectiveness of the so obtained optimal policies are finally presented, showing the proposed model results in economic bene fits for all the users (generators and loads) and in an increased electrical efficiency for the grid.

The lifetime of a wireless sensor network (WSN) is limited by the lifetime of the individual sensor nodes. A promising technique to extend the lifetime of the nodes is wireless energy transfer. The WSN lifetime can also be extended by exploiting the redundancy in the nodes' deployment, which allows the implementation of duty-cycling mechanisms. In this paper, the joint problem of optimal sensor node deployment and WET scheduling is investigated. Such a problem is formulated as an integer optimization whose solution is challenging due to the binary decision variables and non-linear constraints. To solve the problem, an approach based on two steps is proposed. First, the necessary condition for which the WSN is immortal is established. Based on this result, an algorithm to solve the node deployment problem is developed. Then, the optimal WET scheduling is given by a scheduling algorithm. The WSN is shown to be immortal from a networking point of view, given the optimal deployment and WET scheduling. Theoretical results show that the proposed algorithm achieves the optimal node deployment in terms of the number of deployed nodes. In the simulation, it is shown that the proposed algorithm reduces significantly the number of nodes to deploy compared to a random-based approach. The results also suggest that, under such deployment, the optimal scheduling and WET can make WSNs immortal.

Wireless sensor networks (WSNs) consist of energy limited sensor nodes, which limits the network lifetime. Such a lifetime can be prolonged by employing the emerging technology of wireless energy transfer (WET). In WET systems, the sensor nodes can harvest wireless energy from wireless charger, which can use energy beamforming to improve the efficiency. In this paper, a scenario where dedicated wireless chargers with multiple antennas use energy beamforming to charge sensor nodes is considered. The energy beamforming is coupled with the energy consumption of sensor nodes in terms of data routing, which is one novelty of the paper. The energy beamforming and the data routing are jointly optimized by a non-convex optimization problem. This problem is transformed into a semidefinite optimization problem, for which strong duality is proved, and thus the optimal solution exists. It is shown that the optimal solution of the semi-definite programming problem allows to derive the optimal solution of the original problem. The analytical and numerical results show that optimal energy beamforming gives two times better monitoring performance than that of WET without using energy beamforming.

As digital cameras are becoming both cheaper and more advanced, they are also becoming more common both as part of hand-held and consumer devices, and as dedicated surveillance devices. The still images and videos collected by these cameras can be used as input to computer vision algorithms for performing tracking, scene understanding, navigation, etc. The performance of such computer vision tasks can be improved by having multiple cameras observing the same events. However, large scale deployment of camera networks is difficult in areas without access to infrastructure for providing power and network connectivity. In this thesis we consider the use of a network of camera equipped sensor nodes as a cost efficient alternative to conventional camera networks. To overcome the computational limitations of the sensor nodes, we enhance the sensor network with dedicated processing nodes, and process images in parallel using multiple processing nodes.

In the first part of the thesis, we formulate the minimization problem of the time required from image capture until the visual features are extracted from the image. The solution to the minimization problem is an allocation of sub-areas of a captured image to a subset of the processing nodes, which perform the feature extraction. We use the temporal correlation of the image contents to predict an approximation of the distribution of visual features in a captured image. Based on the approximate distribution, we compute an approximate solution to the minimization problem using linear programming. We show that the last value predictor gives a good trade-off between performance and computational complexity.

In the second part of the thesis, we propose fully distributed algorithms for allocation of image sub-areas to the processing nodes in a multi-camera Visual Sensor Network. The algorithms differ in the amount of information available and in how allocation updates are applied. We provide analytical results on the existence of equilibrium allocations, and show that an equilibrium allocation may not be optimal. We show that fully distributed algorithms are most efficient when sensors make asynchronous changes to their allocations, and in topologies with less symmetry. However, with the addition of sparse coordination, both average and worst-case performance can be improved significantly.

Real-time visual analysis tasks, like tracking and recognition, require swift execution of computationally intensive algorithms. Visual sensor networks can be enabled to perform such tasks by augmenting the sensor network with processing nodes and distributing the computational burden in a way that the cameras contend for the processing nodes while trying to minimize their task completion times. In this paper, we formulate the problem of minimizing the completion time of all camera sensors as an optimization problem. We propose algorithms for fully distributed optimization, analyze the existence of equilibrium allocations, evaluate the effect of the network topology and of the video characteristics, and the benefits of central coordination. Our results demonstrate that with sufficient information available, distributed optimization can provide low completion times, moreover predictable and stable performance can be achieved with additional, sparse central coordination.

We consider the problem of assigning communication and computing resources of a fog computing system to visual sensors that may observe various scenes from multiple viewing angles. We formulate the Multi-View Assignment Problem (MVAP) as a quadratic mixed-integer problem, and show that it is NP-hard. We propose a polynomial time 4-approximation based on a transformation of MVAP to a General Assignment Problem with dependent profits in which items are sets of sensors with an overlapping field of view, and based on a reduction of the set of items to be assigned. We show that the reduction of the set of items does not affect the solution of the problem if it results in the dominating set of items. Extensive numerical results show that the proposed algorithm performs close to optimal for small systems, performs well even if the reduction finds an approximately dominating set, and scales well with the number of sensors in the system.

This thesis will investigate the possibility of developing a lightweight digitalcertificate solution for resource constrained embedded systems in 6LoWPANnetworks. Such systems are battery powered or energy harvesting devices whereit is crucial that energy consumption and memory footprints are as minimalas possible. Current solutions for digital certificates are found to be moredemanding than what is desirable and therefore an issue that needs to besolved.The solution that is proposed in this thesis is a profile for the X.509 cer-tificate standard for use with constrained devices and the Internet of Things(IoT). Furthermore, a compression mechanism is designed and implementedfor certificates following this X.509 profile.Results show that compressing certificates is a highly viable solution, de-spite the added complexity it brings.This new lightweight digital certificate solution will allow resource con-strained systems to be able to run for longer without being interrupted orneeding maintenance.

Consider a distributed storage system where parts of the source file fragments in storage nodes are lost. We denote a storage node that lost a part of its fragments as a faulty storage node and a storage node that lost non of its fragment as a complete storage node. In a process, termed as partial repair, a set of storage nodes (among faulty and complete storage nodes) transmit repairing fragments to other faulty storage nodes to recover the lost fragments. We first investigate the optimal partial repair in which the required bandwidth for recovering the lost fragments is minimal. Next, we assume that an eavesdropper wiretaps a subset of links connecting storage nodes, and overhears a number of repairing fragments. We then study optimal secure partial-repair in which the partial-repair bandwidth is minimal and the eavesdropper obtains no information about the source file by overhearing the repairing fragments. We propose optimal secure codes for exact partial-repair in a special scenario.

This chapter is devoted to the use of machine learning (ML) tools to address the spectrum‐sharing problem in cellular networks. The emphasis is on a hybrid approach that combines the traditional model‐based approach with a (ML) data‐driven approach. Taking millimeter‐wave cellular network as an application case, the theoretical analyses and experiments presented in the chapter show that the proposed hybrid approach is a very promising solution in dealing with the key technical aspects of spectrum sharing: the choice of beamforming, the level of information exchange for coordination and association, and the sharing architecture. The chapter then focuses on motivation and background related to spectrum sharing. It also presents the system model and problem formulation, and focuses on all technical aspects of the proposed hybrid approach. Finally, the chapter discusses further issues and conclusions.

MIMO systems in the lower part of the millimetre-wave (mmWave) spectrum band (i.e., below 28 GHz) do not exhibit enough directivity and selectively, as compared to their counterparts in higher bands of the spectrum (i.e., above 60 GHz), and thus still suffer from the detrimental effect of interference, on the system sum rate. As such systems exhibit large numbers of antennas and short coherence times for the channel, traditional methods of distributed coordination are ill-suited, and the resulting communication overhead would offset the gains of coordination. In this paper, we propose algorithms for tackling the sum-rate maximization problem that are designed to address the above-mentioned limitations. We derive a lower bound on the sum rate, a so-called difference of log and trace (DLT) bound, shed light on its tightness, and highlight its decoupled nature at both the transmitters and receivers. Moreover, we derive the solution to each of the subproblems that we dub non-homogeneous waterfilling (a variation on the MIMO waterfilling solution), and underline an inherent desirable feature: its ability to turn-OFF streams exhibiting low SINR, and contribute to greatly speeding up the convergence of the proposed algorithm. We then show the convergence of the resulting algorithm, max-DLT, to a stationary point of the DLT bound. Finally, we rely on extensive simulations of various network configurations, to establish the fast-converging nature of our proposed schemes, and thus their suitability for addressing the short coherence interval, as well as the increased system dimensions, arising when managing interference in lower bands of the mmWave spectrum. Moreover, our results suggest that interference management still brings about significant performance gains, especially in dense deployments.

A secure routing protocol represents a foundational building block of a dependable communication system. Unfortunately, currently no taxonomy exists to assist in the design and analysis of secure routing protocols. Based on the Dagstuhl Seminar 15102, this paper initiates the study of more structured approaches to describe secure routing protocols and the corresponding attacker models, in an effort to better understand existing secure routing protocols, and to provide a framework for designing new protocols. We decompose the routing system into its key components based on a functional model of routing. This allows us to classify possible attacks on secure routing protocols. Using our taxonomy, we observe that the most eective attacks target the information in the control plane. Accordingly, unlike classic attackers whose capabilities are often described in terms of computation complexity we propose to classify the power of an attacker with respect to the reach, that is, the extent to which the attacker can influence the routing information indirectly, beyond the locations under its direct control.

The proliferation of intelligent devices has provisioned more functionality in Critical Infrastructures. But the same automation also brings challenges when it comes to malicious activity, either internally or externally. One such challenge is the attribution of an attack and to ascertain who did what, when and how? Answers to these questions can only be found if the overall underlying infrastructure supports answering such queries. This study sheds light on the power sector specifically on smart grids to learn whether current setups support digital forensic investigations or no. We also address several challenges that arise in the process and a detailed look at the literature on the subject. To facilitate such a study our scope of work revolves around substation automation and devices called intelligent electronic devices (IEDs) in smart grids. 

This is a set of work-in-progress exploratory studies dealing with the log analysis and correlation of very specialized setups in industrial control systems implemented in the context of power systems. These cases consider the behavior of logs and their ability or inability to shed light on the incriminating nature of a criminal investigation. Our research is novel and unique in the sense that no such previous study exists detailing the forensic investigation on ICS within power sector.

A vehicular cloud (VC) is a type of mobile ad hoc cloud in which multiple vehicles share their resources and perform collaborative jobs. In this article, we propose a software-defined (SD) VC (SDVC) architecture to achieve flexible VC control and efficient resource utilization in a centralized manner.

Mobile collaborative community (MCC) is an emerging technology that allows multiple mobile nodes (MNs) to perform a resource intensive task, such as large content download, in a cooperative manner. In this paper, we introduce a proxy-based collaboration system for the MCC where a content proxy (CProxy) determines the amount of chunks and the sharing order scheduled to each MN, and the received chunks are shared among MNs via Wi-Fi Direct. We formulate a multi-objective optimization problem to minimize both the collaborative content download time and the energy consumption in an MCC, and propose a heuristic algorithm for solving the optimization problem. Extensive simulations are carried out to evaluate the effects of the number of MNs, the wireless bandwidth, the content size, and dynamic channel conditions on the content download time and the energy consumption. Our results demonstrate that the proposed algorithm can achieve near-optimal performance and significantly reduce the content download time and has an energy consumption comparable to that of other algorithms.

In this paper, we consider the problem of optimal dynamic service function (SF) placement and flow routing in a SF chaining (SFC) enabled network. We formulate a multi-objective optimization problem to maximize the acceptable flow rate and to minimize the energy cost for multiple service chains. We transform the multi-objective optimization problem into a single-objective mixed integer linear programming (MILP) problem, and prove that the problem is NP-hard. We propose a polynomial time algorithm based on linear relaxation and rounding to approximate the optimal solution of the MILP. Extensive simulations are conducted to evaluate the effects of the energy budget, the network topology, and the amount of server resources on the acceptable flow rate. The results demonstrate that the proposed algorithm can achieve near-optimal performance and can significantly increase the acceptable flow rate and the service capacity compared to other algorithms under an energy cost budget.

Security and privacy are important properties that have to be considered for the adoption of Vehicular Ad-hoc Networks (VANETs). Short-lived credentials, termed pseudonyms, are used to ensure message integrity and authentication while preserving vehicle (thus, their passengers') privacy. However, this introduces extra communication and computation overhead: pseudonyms have to be attached to the messages and signatures on pseudonyms and messages need to be verified before they can be accepted. In this poster, we are concerned with computation overhead for pseudonym validation. We preload vehicular On-Board Units (OBUs) with a Bloom Filter (BF) to facilitate pseudonym validation while traditional approach (i.e., signature verification on pseudonyms) can still be preserved as a fallback approach. We evaluate our scheme on automotive testbed with a preliminary implementation. Our scheme provides low processing delay for pseudonym validation at a cost of communication overhead for pre-downloading the BF.

Security and privacy in Vehicular Ad-hoc Networks (VANETs) mandates use of short-lived credentials (pseudonyms) and cryptographic key pairs. This implies significant computational overhead for vehicles, needing to validate often numerous such pseudonyms within a short period. To alleviate such a bottleneck that could even place vehicle safety at risk, we propose a proactive pseudonym validation approach based on Bloom Filters (BFs). We show that our scheme could liberate computational resources for other (safety- and time-critical) operations with reasonable communication overhead without compromising security and privacy.

Location-based Services (LBSs) provide valuable features but can also reveal sensitive user information. Decentralized privacy protection removes the need for a so-called anonymizer, but relying on peers is a double-edged sword: adversaries could mislead with fictitious responses or even collude to compromise their peers' privacy. We address here exactly this problem: we strengthen the decentralized LBS privacy approach, securing peer-to-peer (P2P) interactions. Our scheme can provide precise timely P2P responses by passing proactively cached Point of Interest (POI) information. It reduces the exposure both to the honest-but-curious LBS servers and peer nodes. Our scheme allows P2P responses to be validated with very low fraction of queries affected even if a significant fraction of nodes are compromised. The exposure can be kept very low even if the LBS server or a large set of colluding curious nodes collude with curious identity management entities.

With the continuous progress on autonomous vehicle and remote drivingtechniques, connection quality demands are changing compared withconventional quality of service. Vehicle to everything communication, asthe connectivity basis for these applications, has been built up on LongTerm Evolution basis, but due to various ethical and environmental issues,few implementations have been made in reality. Therefore simulation approachesare believed to provide valuable insights.To fully model an LTE vehicular network, in this work we first providea comparison study to select the preferable LTE simulator. Aimingto integrate communication nodes with mobility, a solution for simulationframework is developed based on a state-of-art comparison study on theexisting simulator frameworks. We then further develop the network simulator,and complement it with hybrid wireless channel modeling, channeland quality of service aware scheduler, and admission control strategies. Interms of instant optimization of the network, real-time access is emulatedfor external devices to communicate with the simulator. In this thesis,the evaluation of the framework performance considers two aspects: theperformance of the simulator in LTE V2X use case and the feasibility ofthe service, specifically, remote driving, under realistic network capacity.For our framework, the results indicate that it is feasible to realize remotedriving in an LTE urban scenario, but, as an example, we show that foran area of Kista, five vehicles could be hold by a base-station with guaranteedservice at most.

The growing popularity of mobile multimedia content and the increase of wireless access bitrates are straining backhaul capacity in mobile networks. A cost-effective solution to reduce the strain, enabled by emerging all-IP 4G and 5G mobile backhaul architectures, could be in-network caching of popular content during times of peak demand. Motivated by the potential benefits of caching in mobile backhaul networks, in this paper we formulate the problem of content placement in a hierarchical cache network as a binary integer programming problem. We provide a polynomial time solution when the link costs are induced by a potential and we propose a 2-approximation algorithm for the general case. The 2-approximation requires full information about the network topology and the link costs, as well as about the content demands at the different caches, we thus propose two distributed algorithms that are based on limited information on the content demands. We show that the distributed algorithms terminate in a finite number of steps, and we provide analytical results on their approximation ratios. We use simulations to evaluate the proposed algorithms in terms of the achieved approximation ratio and computational complexity on hierarchical cache network topologies as a model of mobile backhaul networks.

Any on-demand pseudonym acquisition strategy is problematic should the connectivity to the credential management infrastructure be intermittent. If a vehicle runs out of pseudonyms with no connectivity to refill its pseudonym pool, one solution is the on-the-fly generation of pseudonyms, e.g., leveraging anonymous authentication. However, such a vehicle would stand out in the crowd: one can simply distinguish pseudonyms, thus signed messages, based on the pseudonym issuer signature, link them and track the vehicle. To address this challenge, we propose a randomized hybrid scheme, RHyTHM, to enable vehicles to remain operational when disconnected without compromising privacy: vehicles with valid pseudonyms help others to enhance their privacy by randomly joining them in using on-the-fly self-certified pseudonyms along with aligned lifetimes. This way, the privacy of disconnected users is enhanced with a reasonable computational overhead. 

Enterprise architecture (EA) has become an essential part of managing technology in large enterprises. These days, automated analysis of EA is gaining increased attention. That is, using models of business and technology combined in order to analyze aspects such as cyber security, complexity, cost, performance, and availability. However, gathering all information needed and creating models for such analysis is a demanding and costly task. To lower the efforts needed a number of approaches have been proposed, the most common are automatic data collection and reference models. However these approaches are all still very immature and not efficient enough for the discipline, especially when it comes to using the models for analysis and not only for documentation and communication purposes. In this paper we propose a format for representing reference models focusing on analysis. The format is tested with a case in a large European project focusing on security in advanced metering infrastructure. Thus we have, based on the format, created a reference model for smart metering architecture and cyber security analysis. On a theoretical level we discuss the potential impact such a reference model can have.

The SCADA infrastructure is a key component for power grid operations. Securing the SCADA infrastructure against cyber intrusions is thus vital for a well-functioning power grid. However, the task remains a particular challenge, not the least since not all available security mechanisms are easily deployable in these reliability-critical and complex, multi-vendor environments that host modern systems alongside legacy ones, to support a range of sensitive power grid operations. This paper examines how effective a few countermeasures are likely to be in SCADA environments, including those that are commonly considered out of bounds. The results show that granular network segmentation is a particularly effective countermeasure, followed by frequent patching of systems (which is unfortunately still difficult to date). The results also show that the enforcement of a password policy and restrictive network configuration including whitelisting of devices contributes to increased security, though best in combination with granular network segmentation.

Bluetooth Low Energy beacons are small transmitters with long battery life that are considered appropriate for providing proximity-based services for businesses. In this work we evaluate experimentally the performance of a proximity-based indoor positioning system built with off-the-shelf beacons in a realistic environment. We demonstrate that the performance of the system depends on a number of factors, such as the displacement between the beacon and the mobile device, the positioning of the beacon as well as the presence and positioning of obstacles such as human bodies. We conclude that algorithms for improving proximity-based indoor positioning need always to be evaluated in realistic scenarios. We believe that the success of commercial context-aware solutions based on BLE beacons is highly dependent on the accuracy required by each application.

Professional audio processing systems such as digital musical instruments, audiomixers, etc. must operate with very tight constraints on overall processing latencyand CPU performance. Consequently, traditional implementations are still mostlybased on specialized hardware like Digital Signal Processors (DSP) and Real-TimeOperating Systems (RTOS) to meet such requirements. However, such systems areminimalistic in nature and they lack many features (e.g. network connectivity, widehardware support, etc.) that a general-purpose operating system such as Linuxoffers. Linux is a very popular choice for the operating system used in embeddeddevices, and many developers have started to use it for designing real-time systemswith relaxed timing constraints. However, none of the available solutions using astandard Linux kernel can satisfy the low-latency requirements of professional audiosystems.In this thesis, a dual kernel approach is employed to enable an embedded Linuxsystem to process audio with low roundtrip latency. The solution is developed usingthe Xenomai framework for real-time computation, which is based on a techniqueknown as interrupt pipeline (I-pipe). I-Pipe enables interrupt virtualization througha micro-kernel running between the Linux kernel and the interrupt controller hardware.The designed system includes an x86 Atom System-on-Chip (SoC), an XMOSmicrocontroller and audio converters to and from the analog domain. Custom kerneldrivers and libraries have been developed to expose the audio programming functionalitiesto programs running in user-space. As a result, the system can achieverobust real-time performance appropriate for professional audio applications, andat the same time it has all the advantages of a traditional Linux solution such ascompatibility with external devices and ease of programming. The real-time capabilityis measured by evaluating the performance in terms of worst case responsetime of the real-time tasks in comparison to the same metrics obtained under astandard Linux kernel. The overall roundtrip latency of audio processing is showedto be improved by almost an order of magnitude (around 2.5ms instead of 20ms).

Architecture models mainly have three functions; 1) document, 2) analyze, and 3) improve the system under consideration. All three functions have suffered from being time-consuming and expensive, mainly due to being manual processes in need of hard to find expertise. Recent work has however automated both the data collection and the analysis. In order for enterprise architecture modeling to finally become free of manual labor the design function also needs to be automated. In this position paper we propose the Automatic Designer. A solution that employs machine learning techniques to realize the design of (near) optimal architecture solutions. This particular implementation is focused on security analysis, but could easily be extended to other topics.

With the proliferation of electric vehicles (EVs), EV battery charging will be a significant load on the power grid, and thus it will have to be optimized. Many proposed optimizations rely on predicted EV information, such as future trip start time and battery State-of-Charge (SoC), for load management, charging scheduling, V2G profit optimization, etc. Prediction in turn would benefit from real-time information about the EVs, while they are on the road, i.e., before arriving at the charging facility. A real-time reporting framework is thus necessary so that EVs can report status information to the utility in a timely fashion. In this paper we present Lynx, an authenticated anonymous real-time reporting protocol. Lynx allows an EV to send anonymous reports using pseudonyms that are unlinkable to its true identity. At the same time, the utility can verify that the reports come from some authentic EV without knowing the exact identity of the EV that sends the report. To encourage EV participation, Lynx allows the utility to issue anonymous receipts to EVs, which can be used later by the EVs to anonymously claim credits. Lynx minimizes computation overhead during real-time reporting, and our implementation on Raspberry Pi 2 shows that report generation and verification can be done within 10 ms.

Dynamic contactless charging is an emerging technology for charging electric vehicles (EVs) on the move. For efficient charging and for proper billing, dynamic charging requires secure communication between the charging infrastructure and the EVs that supports very frequent real-time message exchange for EV authentication. In this paper, we propose Portunes+, an authentication protocol for charging pads to authenticate an EVs identity. Portunes+ uses pseudonyms to provide location privacy, allows EVs to roam between different charging sections and receive a single bill, and achieves fast authentication by relying on symmetric keys and on the spatiotemporal location of the EV. We have implemented Portunes+ on RaspberryPi 2 Model B with 900 MHz CPU and 1 GB RAM. Portunes+ allows the EV to generate authentication information within 0.15 ms and allows charging pads to verify the information within 0.11 ms. In comparison, Elliptic Curve Digital Signature Algorithm signature generation and verification take over 9 ms and over 14 ms, respectively.

Of fundamental importance in networked systems is realtime resource allocation policies. For example, in water distribution systems, water has to be optimally allocated from the produces to the users. In wireless communication systems, radio resources have to be optimally managed at the basestations and access points or user equipments and clients. In SmartGrids, electrical flows have to be optimally allocated from the produces to the consumers. In all these systems, the allocations are done by communicating information over Internet of Things (IoT) networks, serving the Cyberphysical systems, that have limitations such as bandwidth, delay, and losses. Although resource allocation schemes are optimal in theory, in practice they are challenged by the IoT network limitations that can easily cause inaccurate and thus not optimal allocation results. Motivated by theses challenges, we study how the negative effects of time delay can be reduced when solving distributed resource allocation problems by using estimation techniques. In particular, we apply methods used for data fitting, to estimate the delayed current value, based on already arrived signals. These three methods are, respectively, interpolation, least squares and artificial neural networks. Starting from theoretical analysis of signal sequence regularity, interpolation and least squares are proposed. On top of that, the artificial neural network further enables us to preform the prediction without preknowledge of the regularity. We show in numerical simulations that all three methods can largely improve the convergence rate of standard resource allocation algorithms when the communication is delayed. Specically, the convergence rate is remarkably faster, compared with using the latest received data purely to substitute the delayed one, even than the ideal case where there is nodelay. These results imply that methods from numerical analysis and machine learning can be useful tools for predicting delayed signals, when implementing resource allocation algorithms in real-world infrastructures with imperfect communication networks. The thesis contains the following parts: motivation and literature review, background theories, then we apply the three estimation techniques for Lagrangian dual and primal variables prediction, in a specific distributed resource allocation model, and numerical simulation.

The joint scheduling of cellular and D2D communications to share the same radio resource is a complex task.

In one hand, D2D links provide very high throughputs. In the other hand, the intra-cell interference they cause impacts on the performance of cellular communications.

Therefore, designing algorithms and mechanisms that allow an efficient reuse of resources by the D2D links with a reduced impact on cellular communications is a key problem.

In general, traditional Radio Resource Management (RRM) schemes (D2D grouping and mode selection) focus on finding the most compatible D2D pair for an already scheduled cellular User Equipment (UE).

However, such approach limits the number of possible combinations to form the group (composed by a cellular UE and a D2D pair) to be scheduled in the radio resource.

To overcome that, in this work a unified Joint Opportunistic Scheduling (JOS) of cellular and D2D communications, which is able to improve the total system throughput by exploiting the spatial compatibility among cellular and D2D UEs, is proposed.

But more complexity is brought to the scheduling problem.

Thus, a low-complexity suboptimal heuristic Joint Opportunistic Assignment and Scheduling (JOAS) is also elaborated.

Results show that it is possible to reduce the computational complexity but still improve the overall performance in terms of cellular fairness and total system throughput with less impact on cellular communications.

Distributed control and decision making increasingly play a central role in economical and sustainable operation of cyber-physical systems. Nevertheless, the full potential of the technology has not yet been fully exploited in practice due to communication limitations of real-world infrastructures. This work investigates the fundamental properties of gradient methods for distributed optimization, where gradient information is communicated at every iteration, when using limited number of communicated bits. In particular, a general class of quantized gradient methods are studied where the gradient direction is approximated by a finite quantization set. Conditions on the quantization set are provided that are necessary and sufficient to guarantee the ability of these methods to minimize any convex objective function with Lipschitz continuous gradient and a nonempty, bounded set of optimizers. Moreover, a lower bound on the cardinality of the quantization set is provided, along with specific examples of minimal quantizations. Furthermore, convergence rate results are established that connect the fineness of the quantization and number of iterations needed to reach a predefined solution accuracy. The results provide a bound on the number of bits needed to achieve the desired accuracy. Finally, an application of the theory to resource allocation in power networks is demonstrated, and the theoretical results are substantiated by numerical simulations.

The emerging technology of Cyberphysical systems consists of networked computing, sensing, and actuator devices used to monitor, connect, and control physical phenomena. In order to economically and sustainably operate Cyberphysical systems, their devices need to cooperate over a communication network to solve optimization problems. For example, in smart power grids, smart meters cooperatively optimize the grid performance, and in wireless sensor networks a number of sensors cooperate to find optimal estimators of real-world parameters. A challenging aspect in the design of distributed solution algorithms to these optimization problems is that while the technology advances and the networks grow larger, the communication bandwidth available to coordinate the solution remains limited. Motivated by this challenge, this thesis investigates the convergence of distributed solution methods for resource allocation optimization problems, where gradient information is communicated at every iteration, using limited communication. This problem is approached from three different perspectives, each presented in a separate paper.  The investigation of the three papers demonstrate promises and limits of solving distributed resource allocation problems using limited communication bandwidth. Future work will consider how even more general problems can be solved using limited communication bandwidth and also study different communication constraints.

Distributed optimization increasingly plays a centralrole in economical and sustainable operation of cyber-physicalsystems. Nevertheless, the complete potential of the technologyhas not yet been fully exploited in practice due to communicationlimitations posed by the real-world infrastructures. This workinvestigates fundamental properties of distributed optimizationbased on gradient methods, where gradient information iscommunicated using limited number of bits. In particular, ageneral class of quantized gradient methods are studied wherethe gradient direction is approximated by a finite quantizationset. Sufficient and necessary conditions are provided on sucha quantization set to guarantee that the methods minimize anyconvex objective function with Lipschitz continuous gradient anda nonempty and bounded set of optimizers. A lower bound on thecardinality of the quantization set is provided, along with specificexamples of minimal quantizations. Convergence rate results areestablished that connect the fineness of the quantization andthe number of iterations needed to reach a predefined solutionaccuracy. Generalizations of the results to a relevant class ofconstrained problems using projections are considered. Finally,the results are illustrated by simulations of practical systems.

In electricity distribution networks, the increasing penetration of renewable energy generation necessitates faster and more sophisticated voltage controls. Unfortunately, recent research shows that local voltage control fails in achieving the desired regulation, unless there is some communication between the controllers. However, the communication infrastructure for distribution systems are less reliable and less ubiquitous as compared to that for the bulk transmission system. In this paper, we design distributed voltage control that use limited communication. That is, only neighboring buses need to communicate few bits between each other before each control step. We investigate how these controllers can achieve the desired asymptotic behavior of the voltage regulation and we provide upper bounds on the number of bits that are needed to ensure a predefined accuracy of the regulation. Finally, we illustrate the results by numerical simulations.

The need of fast distributed solvers for optimization problems in networked systems has motivated the recent development of the Fast-Lipschitz optimization framework. In such an optimization, problems satisfying certain qualifying conditions, such as monotonicity of the objective function and contractivity of the constraints, have a unique optimal solution obtained via fast distributed algorithms that compute the fixed point of the constraints. This paper extends the set of problems for which the Fast-Lipschitz framework applies. Existing assumptions on the problem form are relaxed and new qualifying conditions are established. It is shown for which cases of more constraints than decision variables, and less constraints than decision variables Fast-Lipschitz optimization applies. New results are obtained by imposing non strict monotonicity of the objective functions. The extended Fast-Lipschitz framework is illustrated by a number of examples, including network optimization and optimal control problems