A Communications-Oriented Perspective on Traffic Management Systems for Smart Cities: Challenges and Innovative Approaches

The growing size of cities and increasing population mobility have determined a rapid increase in the number of vehicles on the roads, which has resulted in many challenges for road traffic management authorities in relation to traffic congestion, accidents, and air pollution. Over the recent years, researchers from both industry and academia have been focusing their efforts on exploiting the advances in sensing, communication, and dynamic adaptive technologies to make the existing road traffic management systems (TMSs) more efficient to cope with the aforementioned issues in future smart cities. However, these efforts are still insufficient to build a reliable and secure TMS that can handle the foreseeable rise of population and vehicles in smart cities. In this survey, we present an up-to-date review of the different technologies used in the different phases involved in a TMS and discuss the potential use of smart cars and social media to enable fast and more accurate traffic congestion detection and mitigation. We also provide a thorough study of the security threats that may jeopardize the efficiency of the TMS and endanger drivers' lives. Furthermore, the most significant and recent European and worldwide projects dealing with traffic congestion issues are briefly discussed to highlight their contribution to the advancement of smart transportation. Finally, we discuss some open challenges and present our own vision to develop robust TMSs for future smart cities

Mobile Autonomous Sensing Unit (MASU): a framework that supports distributed pervasive data sensing

Pervasive data sensing is a major issue that transverses various research areas and application domains. It allows identifying people’s behaviour and patterns without overwhelming the monitored persons. Although there are many pervasive data sensing applications, they are typically focused on addressing specific problems in a single application domain, making them difficult to generalize or reuse. On the other hand, the platforms for supporting pervasive data sensing impose restrictions to the devices and operational environments that make them unsuitable for monitoring loosely-coupled or fully distributed work. In order to help address this challenge this paper present a framework that supports distributed pervasive data sensing in a generic way. Developers can use this framework to facilitate the implementations of their applications, thus reducing complexity and effort in such an activity. The framework was evaluated using simulations and also through an empirical test, and the obtained results indicate that it is useful to support such a sensing activity in loosely-coupled or fully distributed work scenarios.Peer ReviewedPostprint (published version

Software Defined Networks based Smart Grid Communication: A Comprehensive Survey

The current power grid is no longer a feasible solution due to ever-increasing user demand of electricity, old infrastructure, and reliability issues and thus require transformation to a better grid a.k.a., smart grid (SG). The key features that distinguish SG from the conventional electrical power grid are its capability to perform two-way communication, demand side management, and real time pricing. Despite all these advantages that SG will bring, there are certain issues which are specific to SG communication system. For instance, network management of current SG systems is complex, time consuming, and done manually. Moreover, SG communication (SGC) system is built on different vendor specific devices and protocols. Therefore, the current SG systems are not protocol independent, thus leading to interoperability issue. Software defined network (SDN) has been proposed to monitor and manage the communication networks globally. This article serves as a comprehensive survey on SDN-based SGC. In this article, we first discuss taxonomy of advantages of SDNbased SGC.We then discuss SDN-based SGC architectures, along with case studies. Our article provides an in-depth discussion on routing schemes for SDN-based SGC. We also provide detailed survey of security and privacy schemes applied to SDN-based SGC. We furthermore present challenges, open issues, and future research directions related to SDN-based SGC.Comment: Accepte

The effect of workload dependence in systems: Experimental evaluation, analytic models, and policy development

This dissertation presents an analysis of performance effects of burstiness (formalized by the autocorrelation function) in multi-tiered systems via a 3-pronged approach, i.e., experimental measurements, analytic models, and policy development. This analysis considers (a) systems with finite buffers (e.g., systems with admission control that effectively operate as closed systems) and (b) systems with infinite buffers (i.e., systems that operate as open systems).;For multi-tiered systems with a finite buffer size, experimental measurements show that if autocorrelation exists in any of the tiers in a multi-tiered system, then autocorrelation propagates to all tiers of the system. The presence of autocorrelated flows in all tiers significantly degrades performance. Workload characterization in a real experimental environment driven by the TPC-W benchmark confirms the existence of autocorrelated flows, which originate from the autocorrelated service process of one of the tiers. A simple model is devised that captures the observed behavior. The model is in excellent agreement with experimental measurements and captures the propagation of autocorrelation in the multi-tiered system as well as the resulting performance trends.;For systems with an infinite buffer size, this study focuses on analytic models by proposing and comparing two families of approximations for the departure process of a BMAP/MAP/1 queue that admits batch correlated flows, and whose service time process may be autocorrelated. One approximation is based on the ETAQA methodology for the solution of M/G/1-type processes and the other arises from lumpability rules. Formal proofs are provided: both approximations preserve the marginal distribution of the inter-departure times and their initial correlation structures.;This dissertation also demonstrates how the knowledge of autocorrelation can be used to effectively improve system performance, D_EQAL, a new load balancing policy for clusters with dependent arrivals is proposed. D_EQAL separates jobs to servers according to their sizes as traditional load balancing policies do, but this separation is biased by the effort to reduce performance loss due to autocorrelation in the streams of jobs that are directed to each server. as a result of this, not all servers are equally utilized (i.e., the load in the system becomes unbalanced) but performance benefits of this load unbalancing are significant

Privacy in trajectory micro-data publishing : a survey

We survey the literature on the privacy of trajectory micro-data, i.e., spatiotemporal information about the mobility of individuals, whose collection is becoming increasingly simple and frequent thanks to emerging information and communication technologies. The focus of our review is on privacy-preserving data publishing (PPDP), i.e., the publication of databases of trajectory micro-data that preserve the privacy of the monitored individuals. We classify and present the literature of attacks against trajectory micro-data, as well as solutions proposed to date for protecting databases from such attacks. This paper serves as an introductory reading on a critical subject in an era of growing awareness about privacy risks connected to digital services, and provides insights into open problems and future directions for research.Comment: Accepted for publication at Transactions for Data Privac

On I/O Performance and Cost Efficiency of Cloud Storage: A Client\u27s Perspective

Cloud storage has gained increasing popularity in the past few years. In cloud storage, data are stored in the service provider’s data centers; users access data via the network and pay the fees based on the service usage. For such a new storage model, our prior wisdom and optimization schemes on conventional storage may not remain valid nor applicable to the emerging cloud storage. In this dissertation, we focus on understanding and optimizing the I/O performance and cost efficiency of cloud storage from a client’s perspective. We first conduct a comprehensive study to gain insight into the I/O performance behaviors of cloud storage from the client side. Through extensive experiments, we have obtained several critical findings and useful implications for system optimization. We then design a client cache framework, called Pacaca, to further improve end-to-end performance of cloud storage. Pacaca seamlessly integrates parallelized prefetching and cost-aware caching by utilizing the parallelism potential and object correlations of cloud storage. In addition to improving system performance, we have also made efforts to reduce the monetary cost of using cloud storage services by proposing a latency- and cost-aware client caching scheme, called GDS-LC, which can achieve two optimization goals for using cloud storage services: low access latency and low monetary cost. Our experimental results show that our proposed client-side solutions significantly outperform traditional methods. Our study contributes to inspiring the community to reconsider system optimization methods in the cloud environment, especially for the purpose of integrating cloud storage into the current storage stack as a primary storage layer

Private and censorship-resistant communication over public networks

Society’s increasing reliance on digital communication networks is creating unprecedented opportunities for wholesale surveillance and censorship. This thesis investigates the use of public networks such as the Internet to build robust, private communication systems that can resist monitoring and attacks by powerful adversaries such as national governments. We sketch the design of a censorship-resistant communication system based on peer-to-peer Internet overlays in which the participants only communicate directly with people they know and trust. This ‘friend-to-friend’ approach protects the participants’ privacy, but it also presents two significant challenges. The first is that, as with any peer-to-peer overlay, the users of the system must collectively provide the resources necessary for its operation; some users might prefer to use the system without contributing resources equal to those they consume, and if many users do so, the system may not be able to survive. To address this challenge we present a new game theoretic model of the problem of encouraging cooperation between selfish actors under conditions of scarcity, and develop a strategy for the game that provides rational incentives for cooperation under a wide range of conditions. The second challenge is that the structure of a friend-to-friend overlay may reveal the users’ social relationships to an adversary monitoring the underlying network. To conceal their sensitive relationships from the adversary, the users must be able to communicate indirectly across the overlay in a way that resists monitoring and attacks by other participants. We address this second challenge by developing two new routing protocols that robustly deliver messages across networks with unknown topologies, without revealing the identities of the communication endpoints to intermediate nodes or vice versa. The protocols make use of a novel unforgeable acknowledgement mechanism that proves that a message has been delivered without identifying the source or destination of the message or the path by which it was delivered. One of the routing protocols is shown to be robust to attacks by malicious participants, while the other provides rational incentives for selfish participants to cooperate in forwarding messages