| 1. |
- Jeong, Jaeseong, et al.
(författare)
-
Energy-Efficient Wi-Fi Sensing Policy Under Generalized Mobility Patterns With Aging
- 2016
-
Ingår i: IEEE/ACM Transactions on Networking. - : Institute of Electrical and Electronics Engineers (IEEE). - 1063-6692 .- 1558-2566. ; 24:4, s. 2416-2428
-
Tidskriftsartikel (refereegranskat)abstract
- An essential condition precedent to the success of mobile applications based on Wi-Fi (e. g., iCloud) is an energy-efficient Wi-Fi sensing. Clearly, a good Wi-Fi sensing policy should factor in both inter-access point (AP) arrival time (IAT) and contact duration time (CDT) distributions of each individual. However, prior work focuses on limited cases of those two distributions (e. g., exponential) or proposes heuristic approaches such as Additive Increase (AI). In this paper, we first formulate a generalized functional optimization problem on Wi-Fi sensing under general inter-AP and contact duration distributions and investigate how each individual should sense Wi-Fi APs to strike a good balance between energy efficiency and performance, which is in turn intricately linked with users mobility patterns. We then derive a generic optimal condition that sheds insights into the aging property, underpinning energy-aware Wi-Fi sensing polices. In harnessing our analytical findings and the implications thereof, we develop a new sensing algorithm, called Wi-Fi Sensing with AGing (WiSAG), and demonstrate that WiSAG outperforms the existing sensing algorithms up to 37% through extensive trace-driven simulations for which real mobility traces gathered from hundreds of smartphones is used.
|
|
| 2. |
- Choi, Okyoung, et al.
(författare)
-
Delay-Optimal Data Forwarding in Vehicular Sensor Networks
- 2016
-
Ingår i: IEEE Transactions on Vehicular Technology. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9545 .- 1939-9359. ; 65:8, s. 6389-6402
-
Tidskriftsartikel (refereegranskat)abstract
- The vehicular sensor network (VSN) is emerging as a new solution for monitoring urban environments such as intelligent transportation systems and air pollution. One of the crucial factors that determine the service quality of urban monitoring applications is the delivery delay of sensing data packets in the VSN. In this paper, we study the problem of routing data packets with minimum delay in the VSN by exploiting 1) vehicle traffic statistics, 2) anycast routing, and 3) knowledge of future trajectories of vehicles such as busses. We first introduce a novel road network graph model that incorporates the three factors into the routing metric. We then characterize the packet delay on each edge as a function of the vehicle density, speed, and the length of the edge. Based on the network model and delay function, we formulate the packet routing problem as a Markov decision process (MDP) and develop an optimal routing policy by solving the MDP. Evaluations using real vehicle traces in a city show that our routing policy significantly improves the delay performance compared with existing routing protocols. Specifically, optimal VSN data forwarding (OVDF) yields, on average, 96% better delivery ratio and 72% less delivery delay than existing algorithms in some areas distant from destinations.
|
|
| 3. |
- Jeong, Jaeseong, et al.
(författare)
-
Cluster-aided mobility predictions
- 2016
-
Ingår i: Proceedings - IEEE INFOCOM. - : IEEE conference proceedings. - 9781467399531
-
Konferensbidrag (refereegranskat)abstract
- Predicting the future location of users in wireless networks has numerous applications, and can help service providers to improve the quality of service perceived by their clients. The location predictors proposed so far estimate the next location of a specific user by inspecting the past individual trajectories of this user. As a consequence, when the training data collected for a given user is limited, the resulting prediction is inaccurate. In this paper, we develop cluster-aided predictors that exploit past trajectories collected from all users to predict the next location of a given user. These predictors rely on clustering techniques and extract from the training data similarities among the mobility patterns of the various users to improve the prediction accuracy. Specifically, we present CAMP (Cluster-Aided Mobility Predictor), a cluster-aided predictor whose design is based on recent non-parametric Bayesian statistical tools. CAMP is robust and adaptive in the sense that it exploits similarities in users' mobility only if such similarities are really present in the training data. We analytically prove the consistency of the predictions provided by CAMP, and investigate its performance using two large-scale datasets. CAMP significantly outperforms existing predictors, and in particular those that only exploit individual past trajectories.
|
|
| 4. |
- Jeong, Jaeseong, et al.
(författare)
-
TravelMiner : On the benefit of path-based mobility prediction
- 2016
-
Ingår i: 2016 13th Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2016. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509017324
-
Konferensbidrag (refereegranskat)abstract
- Mobility predictions are becoming more valuable in various applications with the rise of mobile devices. Given that existing prediction techniques are composed of two key procedures: 1) profiling past mobility trajectories as sequences of discrete atomic states (e.g., grid locations, semantic locations) and capturing them with an appropriate statistical model, 2) making a prediction on the next state using the statistical model, TravelMiner tackles the former with paths utilized as the atomic states for the first time, where the paths are defined as sub-trajectories with no branches. Comparing to available location-based predictors, TravelMiner makes a fundamental difference in that it is able to predict the sequence of paths rather than locations, which is far more detailed in the perspective of knowing the exact route to follow. TravelMiner enables this benefit by extracting disjoint paths from GPS trajectories via a similarity metric for curves, called Frechet distance and keeping the sequences of such paths in a statistical model, called probabilistic radix tree. Our extensive simulations over the GPS trajectories of 124 users reveal that TravelMiner outperforms other predictors in diverse popular performance metrics including predictability, prediction accuracy and prediction resolution.
|
|
| 5. |
- Jin, Yifei, et al.
(författare)
-
A Graph Attention Learning Approach to Antenna Tilt Optimization
- 2022
-
Ingår i: 2022 1St International Conference On 6G Networking (6GNET). - : Institute of Electrical and Electronics Engineers (IEEE).
-
Konferensbidrag (refereegranskat)abstract
- 6G will move mobile networks towards increasing levels of complexity. To deal with this complexity, optimization of network parameters is key to ensure high performance and timely adaptivity to dynamic network environments. The optimization of the antenna tilt provides a practical and cost-efficient method to improve coverage and capacity in the network. Previous methods based on Reinforcement Learning (RL) have shown effectiveness for tilt optimization by learning adaptive policies outperforming traditional tilt optimization methods. However, most existing RL methods are based on single-cell features representation, which fails to fully characterize the agent state, resulting in suboptimal performance. Also, most of such methods lack scalability and generalization ability due to state-action explosion. In this paper, we propose a Graph Attention Q-learning (GAQ) algorithm for tilt optimization. GAQ relies on a graph attention mechanism to select relevant neighbors information, improving the agent state representation, and updates the tilt control policy based on a history of observations using a Deep Q-Network (DQN). We show that GAQ efficiently captures important network information and outperforms baselines with local information by a large margin. In addition, we demonstrate its ability to generalize to network deployments of different sizes and density.
|
|
| 6. |
- Kim, Y., et al.
(författare)
-
Multi-flow rate control in delayed Wi-Fi offloading systems
- 2016
-
Ingår i: 2016 International Conference on Information Networking (ICOIN). - : IEEE Computer Society. - 9781509017249 ; , s. 274-279
-
Konferensbidrag (refereegranskat)abstract
- Explosive growth of mobile data traffic becomes an increasingly serious problem in cellular networks. Delayed Wi-Fi offloading is the concept to shift the delay-tolerant mobile traffic from cellular networks to Wi-Fi networks at the cost of additional delay. Existing studies mainly focused on a single-flow management or a multi-flow case without specified deadlines. In this paper, we address a multi-flow offloading problem in which a mobile user has multiple traffic flows whose loads and deadlines are different. More precisely, we formulate a multi-flow rate control based on a discrete and finite-horizon Markov decision problem. We develop a dynamic programming (DP)-based optimal rate control algorithm to maximize user satisfaction defined as offloading efficiency minus disutility due to deadline violations. Moreover, we propose a threshold-based rate control algorithm which requires low-complexity and low-memory but achieves high performance. Trace driven simulations based on measurements show that our proposed algorithms achieve high user satisfaction compared to existing algorithms.
|
|
| 7. |
- Lee, Kyunghan, et al.
(författare)
-
Max Contribution : An Online Approximation of Optimal Resource Allocation in Delay Tolerant Networks
- 2015
-
Ingår i: IEEE Transactions on Mobile Computing. - 1536-1233 .- 1558-0660. ; 14:3, s. 592-605
-
Tidskriftsartikel (refereegranskat)abstract
- In this paper, a joint optimization of link scheduling, routing and replication for delay-tolerant networks (DTNs) has been studied. The optimization problems for resource allocation in DTNs are typically solved using dynamic programming which requires knowledge of future events such as meeting schedules and durations. This paper defines a new notion of approximation to the optimality for DTNs, called snapshot approximation where nodes are not clairvoyant, i.e., not looking ahead into future events, and thus decisions are made using only contemporarily available knowledges. Unfortunately, the snapshot approximation still requires solving an NP-hard problem of maximum weighted independent set (MWIS) and a global knowledge of who currently owns a copy and what their delivery probabilities are. This paper proposes an algorithm, Max-Contribution (MC) that approximates MWIS problem with a greedy method and its distributed online approximation algorithm, Distributed Max-Contribution (DMC) that performs scheduling, routing and replication based only on locally and contemporarily available information. Through extensive simulations based on real GPS traces tracking over 4,000 taxies and 500 taxies for about 30 days and 25 days in two different large cities, DMC is verified to perform closely to MC and outperform existing heuristically engineered resource allocation algorithms for DTNs.
|
|
| 8. |
- Vannella, Filippo, et al.
(författare)
-
Best Arm Identification in Multi-Agent Multi-Armed Bandits
- 2023
-
Ingår i: Proceedings of the 40 th International Conference on MachineLearning, Honolulu, Hawaii, USA. - : MLResearchPress. ; , s. 34875-34907
-
Konferensbidrag (refereegranskat)abstract
- We investigate the problem of best arm identification in Multi-Agent Multi-Armed Bandits (MAMABs) where the rewards are defined through afactor graph. The objective is to find an optimalglobal action with a prescribed level of confidenceand minimal sample complexity. We derive a tightinstance-specific lower bound of the sample complexity and characterize the corresponding optimal sampling strategy. Unfortunately, this boundis obtained by solving a combinatorial optimization problem with a number of variables and constraints exponentially growing with the number ofagents. We leverage Mean Field (MF) techniquesto obtain, in a computationally efficient manner,an approximation of the lower bound. The approximation scales at most as ρKd(where ρ, K,and d denote the number of factors in the graph,the number of possible actions per agent, and themaximal degree of the factor graph). We deviseMF-TaS (Mean-Field-Track-and-Stop), an algorithm whose sample complexity provably matchesour approximated lower bound. We illustratethe performance of MF-TaS numerically usingboth synthetic and real-world experiments (e.g.,to solve the antenna tilt optimization problem inradio communication networks).
|
|
| 9. |
- Vannella, Filippo, et al.
(författare)
-
Off-Policy Learning in Contextual Bandits for Remote Electrical Tilt Optimization
- 2023
-
Ingår i: IEEE Transactions on Vehicular Technology. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9545 .- 1939-9359. ; 72:1, s. 546-556
-
Tidskriftsartikel (refereegranskat)abstract
- We investigate the problem of Remote Electrical Tilt (RET) optimization using off-policy learning techniques devised for Contextual Bandits (CBs). The goal in RET optimization is to control the vertical tilt angle of antennas at base stations to optimize key performance indicators representing the Quality of Service (QoS) perceived by the users in cellular networks. Learning an improved tilt update policy is hard. On the one hand, coming up with a policy in an online manner in a real network requires exploring tilt updates that have never been used before, and is operationally too risky. On the other hand, devising this policy via simulations suffers from the simulation-to-reality gap. In this paper, we circumvent these issues by learning an improved policy in an offline manner using existing data collected on real networks. We formulate the problem of devising such a policy using the off-policy contextual bandit framework. We propose CB learning algorithms to extract optimal tilt update policies from the data. We train and evaluate these policies on real-world cellular network data. Our policies show consistent improvements over the rule-based logging policy used to collect the data.
|
|
| 10. |
- Vannella, Filippo, et al.
(författare)
-
Statistical and Computational Trade-off in Multi-Agent Multi-Armed Bandits
- 2023
-
Ingår i: Advances in Neural Information Processing Systems 36 - 37th Conference on Neural Information Processing Systems, NeurIPS 2023. - : Neural Information Processing Systems Foundation.
-
Konferensbidrag (refereegranskat)abstract
- We study the problem of regret minimization in Multi-Agent Multi-Armed Bandits (MAMABs) where the rewards are defined through a factor graph.We derive an instance-specific regret lower bound and characterize the minimal expected number of times each global action should be explored.This bound and the corresponding optimal exploration process are obtained by solving a combinatorial optimization problem whose set of variables and constraints exponentially grow with the number of agents, and cannot be exploited in the design of efficient algorithms.Inspired by Mean Field approximation techniques used in graphical models, we provide simple upper bounds of the regret lower bound.The corresponding optimization problems have a reduced number of variables and constraints.By tuning the latter, we may explore the trade-off between the achievable regret and the complexity of computing the corresponding exploration process.We devise Efficient Sampling for MAMAB (ESM), an algorithm whose regret asymptotically matches the approximated lower bounds.The regret and computational complexity of ESM are assessed numerically, using both synthetic and real-world experiments in radio communications networks.
|
|
