Imperial College London

MrFaheem

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Research Postgraduate
 
 
 
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Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

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8 results found

Zafari F, Li J, Leung KK, Towsley D, Swami Aet al., 2019, Optimal energy consumption with communication, computation, caching and quality guarantee, IEEE Transactions on Control of Network Systems, ISSN: 2325-5870

Energy efficiency is a fundamental requirement of modern data communication systems, and its importance is reflected in much recent work on performance analysis of system energy consumption. However, most work has only focused on communication and computation costs without accounting for data caching costs. Given the increasing interest in cache networks, this is a serious deficiency. In this paper, we consider the problem of energy consumption in data communication, compression and caching (C3) with a quality-of-information (QoI) guarantee in a communication network. Our goal is to identify the optimal data compression rates and cache placement over the network that minimizes the overall energy consumption in the network. We formulate the problem as a Mixed Integer Non-Linear Programming (MINLP) problem with non-convex functions, which is NP-hard in general. We propose a variant of the spatial branch and bound algorithm (V-SBB) that can provide an $\epsilon$ -global optimal solution to the problem. By extensive numerical experiments, we show that the C3 optimization framework improves the energy efficiency by up to 88% compared to any optimization that only considers either communication and caching or communication and computation. Furthermore, the V-SBB technique provides comparatively better solution than some other MINLP solvers at the cost of added computation time.

Journal article

Zafari F, Gkelias A, Leung KK, 2019, A survey of indoor localization systems and technologies, Communications Surveys and Tutorials, Vol: 21, Pages: 2568-2599, ISSN: 1553-877X

Indoor localization has recently witnessed an increase in interest, due to the potential wide range of services it can provide by leveraging Internet of Things (IoT), and ubiquitous connectivity. Different techniques, wireless technologies and mechanisms have been proposed in the literature to provide indoor localization services in order to improve the services provided to the users. However, there is a lack of an up-to-date survey paper that incorporates some of the recently proposed accurate and reliable localization systems. In this paper, we aim to provide a detailed survey of different indoor localization techniques such as Angle of Arrival (AoA), Time of Flight (ToF), Return Time of Flight (RTOF), and Received Signal Strength (RSS); based on technologies such as WiFi, Radio Frequency Identification Device (RFID), Ultra Wideband (UWB), Bluetooth and systems that have been proposed in the literature. The paper primarily discusses localization and positioning of human users and their devices. We highlight the strengths of the existing systems proposed in the literature. In contrast with the existing surveys, we also evaluate different systems from the perspective of energy efficiency, availability, cost, reception range, latency, scalability and tracking accuracy. Rather than comparing the technologies or techniques, we compare the localization systems and summarize their working principle. We also discuss remaining challenges to accurate indoor localization.

Journal article

Zafari F, Li J, Leung KK, Towsley D, Swami Aet al., 2018, A Game-Theoretic Approach to Multi-Objective Resource Sharing and Allocation in Mobile Edge Clouds, EDGETECH'18: PROCEEDINGS OF THE 2018 TECHNOLOGIES FOR THE WIRELESS EDGE WORKSHOP, Pages: 9-13

Journal article

Zafari F, Papapanagiotou I, Christidis K, 2016, Microlocation for Internet-of-Things-Equipped Smart Buildings, IEEE Internet of Things Journal, Vol: 3, Pages: 96-112

Journal article

Li J, Zafari F, Towsley D, Leung KK, Swami Aet al., Joint Data Compression and Caching: Approaching Optimality with Guarantees

We consider the problem of optimally compressing and caching data across acommunication network. Given the data generated at edge nodes and a routingpath, our goal is to determine the optimal data compression ratios and cachingdecisions across the network in order to minimize average latency, which can beshown to be equivalent to maximizing the compression and caching gain under anenergy consumption constraint. We show that this problem is NP-hard in generaland the hardness is caused by the caching decision subproblem, while thecompression sub-problem is polynomial-time solvable. We then propose anapproximation algorithm that achieves a $(1-1/e)$-approximation solution to theoptimum in strongly polynomial time. We show that our proposed algorithmachieve the near-optimal performance in synthetic-based evaluations. In thispaper, we consider a tree-structured network as an illustrative example, butour results easily extend to general network topology at the expense of morecomplicated notations.

Journal article

Zafari F, Leung KK, Towsley D, Basu P, Swami Aet al., A Game-Theoretic Framework for Resource Sharing in Clouds

Providing resources to different users or applications is fundamental tocloud computing. This is a challenging problem as a cloud service provider mayhave insufficient resources to satisfy all user requests. Furthermore,allocating available resources optimally to different applications is alsochallenging. Resource sharing among different cloud service providers canimprove resource availability and resource utilization as certain cloud serviceproviders may have free resources available that can be ``rented'' by otherservice providers. However, different cloud service providers can havedifferent objectives or \emph{utilities}. Therefore, there is a need for aframework that can share and allocate resources in an efficient and effectiveway, while taking into account the objectives of various service providers thatresults in a \emph{multi-objective optimization} problem. In this paper, wepresent a \emph{Cooperative Game Theory} (CGT) based framework for resourcesharing and allocation among different service providers with varyingobjectives that form a coalition. We show that the resource sharing problem canbe modeled as an $N-$player \emph{canonical} cooperative game with\emph{non-transferable utility} (NTU) and prove that the game is convex formonotonic non-decreasing utilities. We propose an $\mathcal{O}({N})$ algorithmthat provides an allocation from the \emph{core}, hence guaranteeing\emph{Pareto optimality}. We evaluate the performance of our proposed resourcesharing framework in a number of simulation settings and show that our proposedframework improves user satisfaction and utility of service providers.

Working paper

Zafari F, Papapanagiotou I, Devetsikiotis M, Hacker Tet al., An iBeacon based Proximity and Indoor Localization System

Indoor localization and Location Based Services (LBS) can greatly benefitfrom the widescale proliferation of communication devices. The basicrequirements of a system that can provide the aforementioned services areenergy efficiency, scalability, lower costs, wide reception range, highlocalization accuracy and availability. Different technologies such as WiFi,UWB, RFID have been leveraged to provide LBS and Proximity Based Services(PBS), however they do not meet the aforementioned requirements. Apple'sBluetooth Low Energy (BLE) based iBeacon solution primarily intends to provideProximity Based Services (PBS). However, it suffers from poor proximitydetection accuracy due to its reliance on Received Signal Strength Indicator(RSSI) that is prone to multipath fading and drastic fluctuations in the indoorenvironment. Therefore, in this paper, we present our iBeacon based accurateproximity and indoor localization system. Our two algorithms Server-SideRunning Average (SRA) and Server-Side Kalman Filter (SKF) improve the proximitydetection accuracy of iBeacons by 29% and 32% respectively, when compared withApple's current moving average based approach. We also present our novelcascaded Kalman Filter-Particle Filter (KFPF) algorithm for indoorlocalization. Our cascaded filter approach uses a Kalman Filter (KF) to reducethe RSSI fluctuation and then inputs the filtered RSSI values into a ParticleFilter (PF) to improve the accuracy of indoor localization. Our experimentalresults, obtained through experiments in a space replicating real-worldscenario, show that our cascaded filter approach outperforms the use of only PFby 28.16% and 25.59% in 2-Dimensional (2D) and 3-Dimensional (3D) environmentsrespectively, and achieves a localization error as low as 0.70 meters in 2Denvironment and 0.947 meters in 3D environment.

Journal article

Panigrahy NK, Li J, Zafari F, Towsley D, Yu Pet al., Optimizing Timer-based Policies for General Cache Networks

Caching algorithms are usually described by the eviction method and analyzedusing a metric of hit probability. Since contents have different importance(e.g. popularity), the utility of a high hit probability, and the cost oftransmission can vary across contents. In this paper, we consider timer-based(TTL) policies across a cache network, where contents have differentiatedtimers over which we optimize. Each content is associated with a utilitymeasured in terms of the corresponding hit probability. We start our analysisfrom a linear cache network: we propose a utility maximization problem wherethe objective is to maximize the sum of utilities and a cost minimizationproblem where the objective is to minimize the content transmission cost acrossthe network. These frameworks enable us to design online algorithms for cachemanagement, for which we prove achieving optimal performance. Informed by theresults of our analysis, we formulate a non-convex optimization problem for ageneral cache network. We show that the duality gap is zero, hence we candevelop a distributed iterative primal-dual algorithm for content management inthe network. Numerical evaluations show that our algorithm significantoutperforms path replication with traditional caching algorithms over somenetwork topologies. Finally, we consider a direct application of our cachenetwork model to content distribution.

Journal article

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