# MrFaheem

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

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### Location

Electrical EngineeringSouth Kensington Campus

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## Publications

Publication Type
Year
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8 results found

Li J, Zafari F, Towsley D, Leung KK, Swami Aet al., 2018, Joint Data Compression and Caching: Approaching Optimality with Guarantees., ICPE, Pages: 229-240

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

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, Li J, Leung KK, Towsley D, Swami Aet al., Optimal Energy Consumption with Communication, Computation, Caching and QoI-Guarantee

Energy efficiency is a fundamental requirement of modern data communicationsystems, and its importance is reflected in much recent work on performanceanalysis of system energy consumption. However, most works have only focused oncommunication and computation costs, but do not account for 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 datacommunication, compression and caching (C$3$) with a Quality of Information(QoI) guarantee in a communication network. {Our goal is to identify theoptimal data compression rate and data placement over the network to minimizethe overall energy consumption in the network.} he formulated problem is a\emph{Mixed Integer Non-Linear Programming} (MINLP) problem with non-convexfunctions, which is NP-hard in general. } {We} propose a variant of spatialbranch and bound algorithm (V-SBB), that can {provide} the $\epsilon$-globaloptimal solution to {our problem}. {We numerically show that our C3optimization framework can improve the energy efficiency up to 88\% compared toany C2 optimization between communication and computation or caching.Furthermore, for our energy consumption problem, V-SBB {provides comparativelybetter solution than some other MINLP solvers.}}

JOURNAL ARTICLE

Zafari F, Li J, Leung KK, Towsley D, Swami Aet al., A Game-Theoretic Approach to Multi-Objective Resource Sharing and Allocation in Mobile Edge Clouds

Mobile edge computing seeks to provide resources to different delay-sensitiveapplications. However, allocating the limited edge resources to a number ofapplications is a challenging problem. To alleviate the resource scarcityproblem, we propose sharing of resources among multiple edge computing serviceproviders where each service provider has a particular utility to optimize. Wemodel the resource allocation and sharing problem as a multi-objectiveoptimization problem and present a \emph{Cooperative Game Theory} (CGT) basedframework, where each edge service provider first satisfies its nativeapplications and then shares its remaining resources (if available) with usersof other providers. Furthermore, we propose an $\mathcal{O}(N)$ algorithm thatprovides allocation decisions from the \emph{core}, hence the obtainedallocations are \emph{Pareto} optimal and the grand coalition of all theservice providers is stable. Experimental results show that our proposedresource allocation and sharing framework improves the utility of all theservice providers compared with the case where the service providers areworking alone (no resource sharing). Our $\mathcal{O}(N)$ algorithm reduces thetime complexity of obtaining a solution from the core by as much as 71.67\%when compared with the \emph{Shapley value}.

JOURNAL ARTICLE

Zafari F, Gkelias A, Leung K, A Survey of Indoor Localization Systems and Technologies

Indoor localization has recently witnessed an increase in interest, due tothe potential wide range of services it can provide by leveraging Internet ofThings (IoT), and ubiquitous connectivity. Different techniques, wirelesstechnologies and mechanisms have been proposed in the literature to provideindoor localization services in order to improve the services provided to theusers. However, there is a lack of an up-to-date survey paper that incorporatessome of the recently proposed accurate and reliable localization systems. Inthis paper, we aim to provide a detailed survey of different indoorlocalization techniques such as Angle of Arrival (AoA), Time of Flight (ToF),Return Time of Flight (RTOF), Received Signal Strength (RSS); based ontechnologies such as WiFi, Radio Frequency Identification Device (RFID), UltraWideband (UWB), Bluetooth and systems that have been proposed in theliterature. The paper primarily discusses localization and positioning of humanusers and their devices. We highlight the strengths of the existing systemsproposed in the literature. In contrast with the existing surveys, we alsoevaluate different systems from the perspective of energy efficiency,availability, cost, reception range, latency, scalability and trackingaccuracy. Rather than comparing the technologies or techniques, we compare thelocalization systems and summarize their working principle. We also discussremaining challenges to accurate indoor localization.

JOURNAL ARTICLE

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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