Is There WiFi Yet?: How Aggressive Probe Requests Deteriorate Energy and Throughput
28 Oct 2015-pp 317-323
TL;DR: Analysis of empirical data collected from large and medium venues shows how aggressive WiFi scans can have significant implications for energy and throughput for mobile nodes, and thoughts on the disjoint incentives for properly balancing WiFi discovery speed and crowded network interactions are closed.
Abstract: WiFi offloading has emerged as a key component of cellular operator strategy to meet the rich data needs of modern mobile devices. Hence, mobile devices tend to aggressively seek out WiFi in order to provide improved user Quality of Experience (QoE) and cellular capacity relief. For home and work environments, aggressive WiFi scans can significantly improve the speed at which mobile nodes join the WiFi network. Unfortunately, the same aggressive behavior that excels in the home environment incurs considerable side effects in crowded wireless environments. In this paper, we analyze empirical data collected from large (stadium) and medium (classroom) venues, and show through controlled experiments (laboratory) how aggressive WiFi scans can have significant implications for energy and throughput for mobile nodes. We close with several thoughts on the disjoint incentives for properly balancing WiFi discovery speed and crowded network interactions.
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28 Nov 2016
TL;DR: The result shows that the proposed approach to extract social behavior and interaction patterns of mobile users by passively monitoring WiFi probe requests and null data frames that are sent by smartphones for network control/management purposes is able to obtain reliable social relationships and interactions in a non-intrusive way.
Abstract: In this paper, we present an approach to extract social behavior and interaction patterns of mobile users by passively monitoring WiFi probe requests and null data frames that are sent by smartphones for network control/management purposes. By analyzing the temporal and spatial correlations of the Receive Signal Strength Indicators (RSSI) of packets from these low rate transmissions, we are able to discover proximity relationships, occupancy patterns, and social interactions among users.We evaluate the SocialProbe system using commodity off-the-shelf smartphones and WiFi Access Points in two locations, a research lab and a public dining area. The result shows that the proposed approach is able to obtain reliable social relationships and interactions in a non-intrusive way.
50 citations
Cites background from "Is There WiFi Yet?: How Aggressive ..."
...CCS Concepts •Networks → Wireless access points, base stations and infrastructure; •Human-centered computing→ Social network analysis; Ubiquitous and mobile computing systems and tools; Smartphones; Keywords Social Relationship; Passive; WiFi Probe; Fingerprinting...
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01 May 2017
TL;DR: Zhang et al. as discussed by the authors carried out a large-scale study on how large the WiFi connection set-up time cost is, what factors affect the Wi-Fi connection setup process, and what can be done to reduce the WiFi Connection Set-Up time cost.
Abstract: Today's WiFi networks deliver a large fraction of traffic. However, the performance and quality of WiFi networks are still far from satisfactory. Among many popular quality metrics (throughput, latency), the probability of successfully connecting to WiFi APs and the time cost of the WiFi connection set-up process are the two of the most critical metrics that affect WiFi users' experience. To understand the WiFi connection set-up process in real-world settings, we carry out measurement studies on 5 million mobile users from 4 representative cities associating with 7 million APs in 0.4 billion WiFi sessions, collected from a mobile “WiFi Manager” App that tops the Android/iOS App market. To the best of our knowledge, we are the first to do such large scale study on: how large the WiFi connection set-up time cost is, what factors affect the WiFi connection set-up process, and what can be done to reduce the WiFi connection set-up time cost. Based on our data-driven measurement and analysis, we reveal the insights as follows: (1) Connection set-up failure and large connection set-up time cost are common in today's WiFi use. As large as 45% of the users suffer connection set-up failures, and 15% (5%) of them have large connection set-up time costs over 5 seconds (10 seconds). (2) Contrary to the state-of-the-art work, scan, one of the subphase of four phases in the connection set-up process, contributes the most (47%) to the overall connection set-up time cost. (3) Mobile device model and AP model can greatly help us to predict the connection set-up time cost if we can make good use of the hidden information. Based on the measurement analysis, we develop a machine learning based AP selection strategy that can significantly improve WiFi connection set-up performance, against the conventional strategy purely based on signal strength, by reducing the connection set-up failures from 33% to 3.6% and reducing 80% time costs of the connection set-up processes by more than 10 times.
38 citations
TL;DR: An experimental analysis of sniffing performance under different wireless environments using off-the-shelf products to identify the possible factors including channel settings and access point configurations that affect sniffing behaviours and performances, thereby enabling the design of a protocol for a WiFi sniffing system under the optimal monitoring strategy in a real deployment.
Abstract: Mobile devices regularly broadcast WiFi probe requests in order to discover available proximal WiFi access points for connection. A probe request, sent automatically in the active scanning mode, consisting of the MAC address of the device expresses an advertisement of its presence. A real-time wireless sniffing system is able to sense WiFi packets and analyse wireless traffic. This provides an opportunity to obtain insights into the interaction between the humans carrying the mobile devices and the environment. Susceptibility to loss of the wireless data transmission is an important limitation on this idea, and this is complicated by the lack of a standard specification for real deployment of WiFi sniffers. In this paper, we present an experimental analysis of sniffing performance under different wireless environments using off-the-shelf products. Our objective is to identify the possible factors including channel settings and access point configurations that affect sniffing behaviours and performances, thereby enabling the design of a protocol for a WiFi sniffing system under the optimal monitoring strategy in a real deployment. Our preliminary results show that four main factors affect the sniffing performance: the number of access points and their corresponding operating channels, the signal strength of the access point and the number of devices in the vicinity. In terms of a real field deployment, we propose assignment of one sniffing device to each specific sub-region based on the local access point signal strength and coverage area and fixing the monitoring channel belongs to the local strongest access point.
28 citations
Cites background from "Is There WiFi Yet?: How Aggressive ..."
...By doing so, a client station can maintain and update a list of known APs [25]....
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18 Sep 2018
TL;DR: CrowdProbe as mentioned in this paper used a Hidden Markov Models (HMM) based trajectory inference algorithm to infer crowd movement using more than 17 million opportunistically obtained probe request frames.
Abstract: Devices with integrated Wi-Fi chips broadcast beacons for network connection management purposes Such information can be captured with inexpensive monitors and used to extract user behavior To understand the behavior of visitors, we deployed our passive monitoring system---CrowdProbe, in a multi-floor museum for six months We used a Hidden Markov Models (HMM) based trajectory inference algorithm to infer crowd movement using more than 17 million opportunistically obtained probe request frames However, as more devices adopt schemes to randomize their MAC addresses in the passive probe session to protect user privacy, it becomes more difficult to track crowd and understand their behavior In this paper, we try to make use of historical transition probability to reason about the movement of those randomized devices with spatial and temporal constraints With CrowdProbe, we are able to achieve sufficient accuracy to understand the movement of visitors carrying devices with randomized MAC addresses
23 citations
TL;DR: Instead of collecting probe requests, SNOW utilizes the WiFi data from smartphones associated with the deployed access points (APs) and applies matrix factorization with a sparsity constraint to derive grouping results directly.
Abstract: Detecting shopping groups is gaining popularity as it enables various applications ranging from marketing to advertising. Existing methods exploit WiFi probe requests to detect shopping groups by identifying co-located customers. However, the probe request is prone to suffer from device heterogeneity which might pose a severe data sparseness problem. More importantly, we find that a certain amount of shopping groups would separate sometimes which makes traditional methods unreliable. In this paper, we propose a shopping group detection system using WiFi (SNOW). Instead of collecting probe requests, SNOW utilizes the WiFi data from smartphones associated with the deployed access points (APs). We could thus obtain data from different devices and even ensure a data granularity of seconds using Arping. Besides, we exploit an effective heuristic extracted from two observations of shopping group dynamics to improve the detection performance. First, the probability of group separation differs in diverse areas. Second, the proportion of group participation and individual engagement differs in different activities of the mall. Therefore, APs under which shopping groups appear more frequently and barely separate should contribute more in measuring customer similarity. Lastly, we represent the measured similarity into a matrix format and apply matrix factorization with a sparsity constraint to derive grouping results directly. According to our experiments in a large shopping mall, SNOW improves the detection performance of baseline approaches by 13.2% on average.
23 citations
Additional excerpts
...80% of the devices reply with empty SSID list [28], approaches that rely on SSID may not work well anymore....
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References
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TL;DR: This paper discusses all of these topics, identifying key challenges for future research and preliminary 5G standardization activities, while providing a comprehensive overview of the current literature, and in particular of the papers appearing in this special issue.
Abstract: What will 5G be? What it will not be is an incremental advance on 4G. The previous four generations of cellular technology have each been a major paradigm shift that has broken backward compatibility. Indeed, 5G will need to be a paradigm shift that includes very high carrier frequencies with massive bandwidths, extreme base station and device densities, and unprecedented numbers of antennas. However, unlike the previous four generations, it will also be highly integrative: tying any new 5G air interface and spectrum together with LTE and WiFi to provide universal high-rate coverage and a seamless user experience. To support this, the core network will also have to reach unprecedented levels of flexibility and intelligence, spectrum regulation will need to be rethought and improved, and energy and cost efficiencies will become even more critical considerations. This paper discusses all of these topics, identifying key challenges for future research and preliminary 5G standardization activities, while providing a comprehensive overview of the current literature, and in particular of the papers appearing in this special issue.
7,139 citations
"Is There WiFi Yet?: How Aggressive ..." refers background in this paper
...Although LTE-Advanced (LTE-A) will offer relief with the introduction of small cell support, questions remain with regards to small cell economic viability and management complexity [1]....
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01 Oct 2004
TL;DR: The pitfalls that an actual wireless monitoring system for an IEEE 802.11 based wireless network needs to be aware of are identified, and feasible solutions to avoid those pitfalls are provided.
Abstract: Many studies on measurement and characterization of wireless LANs (WLANs) have been performed recently. Most of these measurements have been conducted from the wired portion of the network based on wired monitoring (e.g. sniffer at some wired point) or SNMP statistics. More recently, wireless monitoring, the traffic measurement from a wireless vantage point, is also widely adopted in both wireless research and commercial WLAN management product development. Wireless monitoring technique can provide detailed PHY/MAC information on wireless medium. For the network diagnosis purpose (e.g. anomaly detection and security monitoring) such detailed wireless information is more useful than the information provided by SNMP or wired monitoring. In this paper we have explored various issues in implementing the wireless monitoring system for an IEEE 802.11 based wireless network. We identify the pitfalls that such system needs to be aware of, and then provide feasible solutions to avoid those pitfalls. We implement an actual wireless monitoring system and demonstrate its effectiveness by characterizing a typical computer science department WLAN traffic. Our characterization reveals rich information about the PHY/MAC layers of the IEEE 802.11 protocol such as the typical traffic mix of different frame types, their temporal characteristics and correlation with the user activities. Moreover, we identify various anomalies in protocol and security of the IEEE 802.11 MAC. Regarding the security, we identify malicious usages of WLAN, such as email worm and network scanning. Our results also show excessive retransmissions of some management frame types reducing the useful throughput of the wireless network.
207 citations
Additional excerpts
...Monitoring also plays a key role in distinguishing performance issues with Yeo in [10] and more contemporary work by Rayanchu et....
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22 Jun 2009
TL;DR: Blue-Fi is presented, a sytem that predicts the availability of the Wi-Fi connectivity by using a combination of bluetooth contact-patterns and cell-tower information, thus avoiding the long periods in idle state and significantly reducing the the number of scans for discovery.
Abstract: Mobile devices are increasingly equipped with multiple network interfaces with complementary characteristics. In particular, the Wi-Fi interface has high throughput and transfer power efficiency, but its idle power consumption is prohibitive. In this paper we present, Blue-Fi, a sytem that predicts the availability of the Wi-Fi connectivity by using a combination of bluetooth contact-patterns and cell-tower information. This allows the device to intelligently switch the Wi-Fi interface on only when there is Wi-Fi connectivity available, thus avoiding the long periods in idle state and significantly reducing the the number of scans for discovery.Our prediction results on traces collected from real users show an average coverage of 94% and an average accuracy of 84%, a 47% accuracy improvement over pure cell-tower based prediction, and a 57% coverage improvement over the pure bluetooth based prediction. For our workload, Blue-Fi is up to 62% more energy efficient, which results in increasing our mobile device's lifetime by more than a day.
160 citations
"Is There WiFi Yet?: How Aggressive ..." refers background in this paper
...While most practitioners in the wireless field would surmise such excessive Probe Requests to be a problem [7, 8], the degree to which such Probe Requests clutter the network in today’s wireless devices is simply stunning....
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...[7] G. Ananthanarayanan and I. Stoica, “Blue-Fi: Enhancing Wi-Fi Performance Using Bluetooth Signals,” in MobiSys 2009, ACM, 2009, pp. 249–262....
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...Wu et. al proposed the concept of Footprint [8] while Ananthanarayanan and Stoica proposed Blue-Fi [7] for the express purpose of using cellular (Footprint) or Bluetooth (Blue-Fi) to efficiently guide WiFi scans....
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...al proposed the concept of Footprint [8] while Ananthanarayanan and Stoica proposed Blue-Fi [7] for the express purpose of using cellular (Footprint) or Bluetooth (Blue-Fi) to efficiently guide WiFi scans....
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18 Jun 2007
TL;DR: The impact of scan operations and related issues in the energy consumption on WiFi phones are quantified through actual measurements and derived guidelines are provided for improving the power conservation approaches in WiFi- based phones from the first of its kind experimental study.
Abstract: WiFi based phones are becoming increasingly popular due to the ubiquitous presence of wireless LANs and the use of unlicensed spectrum. These phones use VoIP techniques over wireless LANs. In addition to the spectral efficiency and security issues, energy consumption is a vital issue in making the usage of these phones widespread. Efforts are underway in improving energy conservation in these phones and thus increasing the duration between recharging the battery. This paper provides a detailed anatomy of the energy consumption by various components of WiFi-based phones. Through a measurement-based study of WiFi-based phones, we have analyzed the energy consumption for various workloads at various components. The impact of scan operations and related issues in the energy consumption on WiFi phones are quantified through actual measurements. Several inferences and derived guidelines are provided for improving the power conservation approaches in WiFi- based phones from the first of its kind experimental study.
91 citations
"Is There WiFi Yet?: How Aggressive ..." refers background in this paper
...In their work, Gupta and Mohapatra [12] focused specifically on the power consumption of WiFi on phones while the work by Raghavendra et. al in [5] and Gupta et. al in [6] looked at larger scale venues (i.e., conferences) and overall performance....
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...In their work, Gupta and Mohapatra [12] focused specifically on the power consumption of WiFi on phones while the work by Raghavendra et....
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...[12] A. Gupta and P. Mohapatra, “Energy Consumption and Conservation in WiFi Based Phones: A Measurement-Based Study,” in SECON 2007, IEEE, June 2007, pp. 122–131....
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19 Apr 2009
TL;DR: This paper studies the handoff process in large AP-dense 802.11 networks, which is one of the most common forms of WiFi under usage, and develops a solution to significantly improve the essential process of AP scan, a bottleneck towards fast and smooth handoffs.
Abstract: wireless networks have gained ever greater popularity nowadays. Apart from static wireless connections, people begin to expect more user-friendly features from this kind of networks, such as support for seamless roaming. In this paper, we study the handoff process in large AP-dense 802.11 networks, which is one of the most common forms of WiFi under usage. A series of field experiments are carried out and some critical handoff parameters are evaluated. With some newly discovered features, i.e. differentiated probe response time and rich AP information hidden in wireless traffic, we have managed to sig- nificantly improve the essential process of AP scan, a bottleneck towards fast and smooth handoffs. The solution is collectively called D-Scan (Scan in AP-Dense 802.11 networks). Real experi- ments are conducted to show the superiority of our solution.
58 citations
"Is There WiFi Yet?: How Aggressive ..." refers background in this paper
...For the purposes of this paper though, we are chiefly concerned with works focusing on increased discovery speed [9] and most notably, improved efficiency or accuracy for WiFi scanning [5–8, 10–12]....
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...al in [9] proposed D-Scan, specifically targeted at improving scan efficiency in dense environments....
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