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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10 results found

Zafari F, Li J, Leung KK, Towsley D, Swami Aet al., 2020, Optimal energy consumption with communication, computation, caching and quality guarantee, IEEE Transactions on Control of Network Systems, Vol: 7, Pages: 151-162, 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, Technologies for the Wireless Edge Workshop (EdgeTech), Publisher: ASSOC COMPUTING MACHINERY, Pages: 9-13

Conference paper

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, Leung KK, Towsley D, Basu P, Swami A, Li Jet al., Let's Share: A Game-Theoretic Framework for Resource Sharing in Mobile Edge Clouds

Mobile edge computing seeks to provide resources to different delay-sensitiveapplications. This is a challenging problem as an edge cloud-service providermay not have sufficient resources to satisfy all resource requests.Furthermore, allocating available resources optimally to different applicationsis also challenging. Resource sharing among different edge cloud-serviceproviders can address the aforementioned limitation as certain serviceproviders may have resources available that can be ``rented'' by other serviceproviders. However, edge cloud service providers can have different objectivesor \emph{utilities}. Therefore, there is a need for an efficient and effectivemechanism to share resources among service providers, while considering thedifferent objectives of various providers. We model resource sharing as amulti-objective optimization problem and present a solution framework based on\emph{Cooperative Game Theory} (CGT). We consider the strategy where eachservice provider allocates resources to its native applications first andshares the remaining resources with applications from other service providers.We prove that for a monotonic, non-decreasing utility function, the game iscanonical and convex. Hence, the \emph{core} is not empty and the grandcoalition is stable. We propose two algorithms \emph{Game-theoretic Paretooptimal allocation} (GPOA) and \emph{Polyandrous-Polygamous Matching basedPareto Optimal Allocation} (PPMPOA) that provide allocations from the core.Hence the obtained allocations are \emph{Pareto} optimal and the grandcoalition of all the service providers is stable. Experimental results confirmthat our proposed resource sharing framework improves utilities of edgecloud-service providers and application request satisfaction.

Journal article

Zafari F, Basu P, Leung KK, Li J, Swami A, Towsley Det al., Resource Sharing in the Edge: A Distributed Bargaining-Theoretic Approach

The growing demand for edge computing resources, particularly due toincreasing popularity of Internet of Things (IoT), and distributed machine/deeplearning applications poses a significant challenge. On the one hand, certainedge service providers (ESPs) may not have sufficient resources to satisfytheir applications according to the associated service-level agreements. On theother hand, some ESPs may have additional unused resources. In this paper, wepropose a resource-sharing framework that allows different ESPs to optimallyutilize their resources and improve the satisfaction level of applicationssubject to constraints such as communication cost for sharing resources acrossESPs. Our framework considers that different ESPs have their own objectives forutilizing their resources, thus resulting in a multi-objective optimizationproblem. We present an $N$-person \emph{Nash Bargaining Solution} (NBS) forresource allocation and sharing among ESPs with \emph{Pareto} optimalityguarantee. Furthermore, we propose a \emph{distributed}, primal-dual algorithmto obtain the NBS by proving that the strong-duality property holds for theresultant resource sharing optimization problem. Using synthetic and real-world data traces, we show numerically that theproposed NBS based framework not only enhances the ability to satisfyapplications' resource demands, but also improves utilities of different ESPs.

Journal article

Panigrahy NK, Li J, Zafari F, Towsley D, Yu Pet al., A TTL-based Approach for Content Placement in Edge Networks

Edge networks are promising to provide better services to users byprovisioning computing and storage resources at the edge of networks. However,due to the uncertainty and diversity of user interests, content popularity,distributed network structure, cache sizes, it is challenging to decide whereto place the content, and how long it should be cached. In this paper, we studythe utility optimization of content placement at edge networks throughtimer-based (TTL) policies. We propose provably optimal distributed algorithmsthat operate at each network cache to maximize the overall network utility. OurTTL-based optimization model provides theoretical answers to how long eachcontent must be cached, and where it should be placed in the edge network.Extensive evaluations show that our algorithm significantly outperforms pathreplication with conventional caching algorithms over some network topologies.

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

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

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