6 results found
Li J, Zafari F, Towsley D, et al., 2018, Joint Data Compression and Caching: Approaching Optimality with Guarantees., ICPE, Pages: 229-240
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
Panigrahy NK, Li J, Zafari F, et 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.
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.
Zafari F, Li J, Leung KK, et al., Optimal Energy Tradeoff among Communication, Computation and Caching with QoI-Guarantee
Many applications must ingest and analyze data that are continuouslygenerated over time from geographically distributed sources such as users,sensors and devices. This results in the need for efficient data analytics ingeo-distributed systems. Energy efficiency is a fundamental requirement inthese geo-distributed data communication systems, and its importance isreflected in much recent work on performance analysis of system energyconsumption. However, most works have only focused on communication andcomputation costs, and do not account for caching costs. Given the increasinginterest in cache networks, this is a serious deficiency. In this paper, weconsider the energy consumption tradeoff among communication, computation, andcaching (C3) for data analytics under a Quality of Information (QoI) guaranteein a geo-distributed system. To attain this goal, we formulate an optimizationproblem to capture the C3 costs, which turns out to be a non-convex MixedInteger Non-Linear Programming (MINLP) Problem. We then propose a variant ofspatial branch and bound algorithm (V-SBB), that can achieve e-global optimalsolution to the original MINLP. We show numerically that V-SBB is more stableand robust than other candidate MINLP solvers under different networkscenarios. More importantly, we observe that the energy efficiency under our C3optimization framework improves by as much as 88% compared to any C2optimization between communication and computation or caching.
Zafari F, Papapanagiotou I, Devetsikiotis M, et 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.
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