Can Somebody Explain About Wi-Fi Device Manager Settings And Power Settings In Windows 7?
Sometimes I have low Wi-Fi signal and I am curious about the Wi-Fi settings in Device Manager and Power Settings. I would like to get an exhaustive explanation about each, so let’s start with Wi-Fi settings in Device Manager: 
Screen continued: 
I would like to understand every property in the «Property» scroll pane. Now let’s look at Power Settings: 
I would like to know what impact the selected options have on Wi-Fi. I’m running Windows 7 64-bit Enterprise with a Broadcom DW1530 Wi-Fi chipset.
- wireless-networking
- power-management
- network-adapter
- device-manager
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asked Oct 4, 2011 at 8:00
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So you’re asking for the definition of >20 terms?
Oct 4, 2011 at 9:58
Those device manager items are better left alone, default settings are optimal, your wifi problem is something else, possibly interference which can be dealt with in the wireless router by changing the radio channel to a more stable one.
Oct 4, 2011 at 16:20
I know these settings are optimal, that is why i want to know «what is under the hood» if you know what i mean. I want to know the car i am driving.
Oct 6, 2011 at 4:00
It probably would’ve been best to split this up into two questions: 1 on power and the other on wi-fi
Oct 12, 2011 at 15:18
3 Answers 3
The gest is that you can leave most settings to their default, unless:
- You attempt to improve reception/throughput when it’s performing bad.
- You need to comply to regulations, laid by your company or country or other rules/laws.
- You need to change specific options like «disable upon wired», «mac address» when your network driver doesn’t provide you with a more easy Configuration GUI.
Note: I don’t know your background, but if a term is unclear then Wikipedia helps a lot.
Here is the list explained from my point of view:
- 802.11h+d This option restricts your card to either 802.11h , 802.11d or both; which are under certain regulations. For example, 802.11h is designed to comply with European regulations. If you want to comply to those, this option is for you; but in general, I live there and I’m just using 802.11n .
- Afterburner Only when you have an 802.11g network, enabling this option on both the router as your laptop can result in a better throughput. You might want to verify change with a speed and ping test though. Be sure to read the documentation provided by both your wireless card and router for an explanation and to check compatibility.
- Antenna Diversity This only applies if you have two antennas, you can select which antenna to use. However, you should probably leave this to the default which automatically switches between both antennas based on the signal strength.
- AP Compatibility Mode If you have a very old router, this option will trade performance for compatibility. You don’t need this option if you are already able to internet with your network card and are connected to the right AP.
- Band Preference This option might be handy if you have interference at home on the 2.4 GHz or 5 GHz band or need to comply to regulations that restrict the use of a band; it’s best to leave this to its default so that you can connect to both as you can just configure the band on the router to avoid interference.
- Bandwidth Capability Within a frequency range like 2.4 GHz or 5 GHz, 20 MHz stands for a single channel while 40 MHz will take multiple neighboring channels. As this again can be configured in the router, you can leave this to the default as you most likely don’t want to restrict the compatibility of your card.
- Bluetooth Collaboration This avoids your WiFi and Bluetooth from interfering each other by suppressing each others signal when they are both sending something. Unless you have throughput problems on either, it’s best to leave this option enabled.
- BSS Mode This can again be used to restrict your card to 802.11b/g or 802.11b , it’s best to leave this option default for compatibility reasons unless you need to change them under certain regulations.
- BT-AMP This amplifies Bluetooth by sending it over an 802.11 link, where you can get 10x speed compared to the Bluetooth standard. You could try to play with this option if you need improved Bluetooth performance.
- Disable Bands Rather than giving a band preference, this actually disables a band. This thus has the same reasoning as listed under the «Band Preference» bullet point; use your router instead.
- Disable Upon Wired Connection Does what it says. It’s up to yourself to see what works best if you have this use case.
- Fragmentation Threshold The size at which a packet is fragmented into multiple packets, see MTU for more details. In the past, I usually have set this to 1492 given that’s the maximum my connection could support; but now I’m using jumbo frames on my network so I removed the limit again. You can determine the largest MTU possible for your connection and optionally change this option. You might want to test just like I mentioned in the «Afterburner» bullet point.
- IBSS 54g Protection Mode Although a weird name, this is an implementation of 802.11 RTS/CTS which is only enabled when an 802.11b node joins an ad hoc network; if you are sure there won’t be such nodes you could disable this option, but given that it’s automatic you can leave it default.
- IBSS Mode Most likely you are not using an ad hoc network, but this allows you to select whether to use 802.11b or 802.11g in that case. In a normal use case you don’t need to change this setting.
- Locally Administered MAC Address Allows you to change the MAC Address of your wireless network card, please note that they must remain unique. I would suggest against changing this, unless you need it for one or another reason.
- Minimum Power Consumption This will stop scanning for networks or turn off the camera when you disconnect from a network or when your laptop is idle. This is enabled by default, this might help the network card to reconnect when the signal is low so you might want to try to disable it.
- PLCP Header This sets the Complimentary Code Keying header, by default it automatically switched between long and short based on the situation the card is in. It’s best to leave it like this as it removes overhead in some situations, in extreme occasions it might be necessary to set this to long.
- Priority & VLAN By default, the packets in the queue are transmitted on a first-come, first-served basis, regardless of any priority information within the packet. When enabling this setting you can give certain classes [background (BG), best-effort (BE), video (VI), and voice (VO)] a priority in the queue. Then you can optionally choose whether the VLAN has priority or not. This setting is related to QoS, it doesn’t help with low signal problems but rather when you want to attempt to improve throughput of certain classes.
- Rate (802.11a) Here, you can limit the rate. You should not need to do this.
- Rate (802.11b/g) Here, you can limit the rate. You should not need to do this.
- Roam Tendency This setting allows to roam (reconnect) to a different wireless router/AP if the signal difference is significant, thus it only applies when you have different wireless router/APs providing the same work (like in a university or big company). The default is set to a difference of 20 dB, aggressive will set this to 10 dB and conservative sets this to 30 dB. The names of these options sure have a meaning, note that changing between wireless router/APs isn’t instant.
- Roaming Decision This decides when it will start to roam; it is the signal strength value that determines when the WLAN card starts scanning for another wireless router/AP. The default is 75 dB, you can choose to optimize bandwidth (65 dB) or optimize distance (85 dB). Just like Roam Tendency, this setting only matters when you have different wireless router/APs available.
- RTS Threshold RTS stands for «Request to Send», this setting controls at what packet size the low level protocol issues an RTS packet. The default is 2346. NetGear lists several trade-offs to consider setting this parameter:
Using a small value causes RTS packets to be sent more often, consuming more of the available bandwidth, therefore reducing the apparent throughput of the network packet. However, the more RTS packets that are sent, the quicker the system can recover from interference or collisions — as would be the case in a heavily loaded network, or a wireless network with much electromagnetic interference.
As for the power settings, it changes the amount of power the network card use. So, as you configured it for battery it would go into a lower power state; when on battery this could result in less reception, less stronger output signal, less throughput and so on.
Why do estimates for internet energy consumption vary so drastically?
A few years ago, we decided that as a sustainable business we must include the carbon footprint of our digital products in our overall carbon footprint, but at that time there was no known way to do this.
We set out to create a method for quantifying the carbon emissions of websites and eventually created the first public carbon calculator for websites at WebsiteCarbon.com. Now in its second iteration, this tool has come close to completing a million tests, helped engage people in the topic of website emissions and inspired digital teams to pursue higher levels of efficiency.
To create it, we faced a big challenge in how to correlate a web page (formed of HTML, CSS, JavaScript, images and videos), into a figure of CO2 emissions. After studying the existing literature, we saw that energy consumption (easily converted to CO2) could be estimated from the amount of data transferred. This link between data transfer (GB) and energy consumption (kWh) made the impossible task of quantifying website emissions, possible.
However, figures for the amount of energy used per gigabyte of data transferred vary enormously. Now that digital sustainability is becoming a more mainstream topic, I’m seeing an increasing number of articles and media reports using vastly different figures in reference to the energy consumption of digital services, which I feel will inevitably lead to confusion if the differences are not clearly explained.
In this post, I hope to shed some light on why the data varies so much, how to interpret numbers quoted in articles, and how to make an informed decision about the data you use in calculating your own emissions for digital projects.
Orders of magnitude
Reviewing the academic literature on the energy consumption of internet data, we found that figures varied from the lowest of 0.004 kWh/GB to 136 kWh/GB. In other words, the estimates varied by several orders of magnitude. What on earth was going on?
In 2017, a meta-analysis by the name of Electricity Intensity of Internet Data Transmission: Untangling the Estimates, was published with the aim of making sense of these huge differences. Having reviewed 14 existing studies on this topic, the authors concluded that an accurate estimate for electricity used to transmit data through the internet was 0.06 kWh/GB for 2015.
Case closed, right?
Well, not so fast.
The importance of system boundaries
One of the key variables in these studies is the system boundary, or put simply, which parts of the total system are actually being studied. The meta-analysis filtered the data to only look at the smallest possible sub-system, representing the network equipment used for data transmission and access at a national level. In other words, they adjusted all of the studies to only look at the energy used to make a gigabyte of data travel through a telecom network within a national cable network.
This is a useful figure to have, especially as a component of larger life cycle analyses, but it inherently gives an incomplete picture. It entirely ignores important parts of the overall system including data centers, international infrastructure, on-site networking equipment and end user devices, not to mention the differences between cable and mobile networks.
And there is more. The system boundaries in the diagram above, which appears to be the whole picture, do not show the embodied energy of building the data centers, manufacturing the servers, constructing the cable and wireless network infrastructure and manufacturing end user devices. Some argue that this is not relevant, while others argue that this is inherently part of the total emissions, especially as servers get replaced every 3-4 years and we are building and upgrading infrastructure rapidly to feed our hunger for data. When this is factored in, the full picture of energy and carbon emissions from digital services looks far bigger.
System boundaries are also important when looking at a project’s carbon footprint for reporting and offsetting purposes. Some organisations have a policy to report all carbon emissions on scopes 1, 2 and 3, in which case they would want to use wide system boundaries. On the other hand, some take a more limited view of where their responsibility for emissions ends and would therefore choose more limited system boundaries.
So what is the correct set of system boundaries?
All of them. Or none of them!
There is no defacto set of system boundaries that we should use for every scenario and that’s part of the reason that none of this is as simple as we all wish it to be. The appropriate boundaries are entirely dependent on what it is we are trying to learn. Whether we are doing the reporting or reading other’s reports, it is important to know where these boundaries are drawn, and perhaps more importantly, why.
Other factors
The meta-analysis also highlighted that the date of the studies has an impact on the estimates produced, with more recent studies tending to estimate lower energy per gigabyte as technology becomes more efficient. It is therefore important to know the year of the data that was used to do the calculations. For example, I might be estimating emissions for a web project now in 2020, but the most recent reliable study might only include data for 2017. I could choose to use data from a past year, or I may choose to adjust the data myself to factor in gains in efficiency. What is important is that these details are stated so that the numbers are transparent to the people viewing them.
Interestingly, the meta-analysis also reported that study methodology does not make a huge difference to the overall estimates of internet energy use, confirming that system boundaries and date are the major factors.
How does this relate to website emissions?
If we want to know the energy required to make data flow through a cable then, as the meta-analysis did, we need to use narrow system boundaries.
However, if we want to understand the bigger picture of the total emissions associated with a website or web service, then we need to be using the widest possible system boundaries. It’s horses for courses, and we need to set our system boundaries appropriate to the application we are studying.
In the case of our work at Wholegrain, we want to understand the total impact of websites. For this reason, we look to use studies with wide system boundaries, with WebsiteCarbon.com currently based on the study On Global Electricity Usage of Communication Technology: Trends to 2030 and an energy factor of 1.8 kWh/GB for 2017. This is a lot higher than the narrow system boundary estimate of 0.06 kWh/GB, but is actually at the lower end of estimates that include complete system boundaries.
On this basis, we feel fairly comfortable that the figure we are using is a reasonably accurate figure for what we are trying to represent. We hope to soon update this to use a figure for 2020/21, but ideally would like to see a new study covering the full system boundaries to provide this updated data.
Is kWh/GB a suitable metric?
There is debate over whether kWh/GB is a suitable metric for estimating the energy use of anything beyond making data travel through a cable. For example, the energy used in the data center is not necessarily directly proportional to the amount of data transferred because it depends on the amount of processing that the servers need to do when processing each request.
Likewise, there are arguments that energy for onsite networking equipment and end user devices should be measured per hour and not per GB. To complicate things further, the distance between the data center and end user can make a big difference, and yet isn’t represented as a variable when using a standard figure for energy per gigabyte.
These are all fair comments and in an ideal world, we would factor in every possible variable. However, the internet by nature is a hugely complex system in which it is impossible to measure a lot of the individual variables accurately. Even in cases where we could, it would require a complex, time consuming and expensive study to do so. This is simply not practical in most real applications, where time is short and there is no budget for calculating digital carbon emissions.
In order for us to take practical action to reduce website carbon emissions, we need to have a simple, standardised method of quantifying impact on a like-for-like basis. Energy per gigabyte is the simplest way to do that, and we have the benefit that several studies have quantified the full system on that basis.
Of course, we can pursue even greater levels of accuracy by making the effort to calculate some of the other variables, which I have been doing on the side, but it becomes hugely more complex and difficult to use as a method in real web projects. And that is what we need, practical tools and methods to drive improvement in real life web projects.
We must accept that no estimate of internet or website carbon emissions will ever be perfect – that’s why they’re called estimates. What matters is that we have a methodology that helps drive improvement, based on data from credible sources, with system boundaries that are appropriate to our needs, and that we clearly state our own assumptions.
Conflicts of interest
I mentioned that we should take our data from credible sources. In practice, most studies on this topic are going to be objective and trustworthy, but it’s worth being aware of potential conflicts of interest, even if genuinely unintentional. With industries like energy, transport, and food coming under mounting pressure to reduce carbon emissions, it seems reasonable to assume that the big tech and telecoms companies would be keen to avoid their own industries coming into the spotlight in a similar way. There is a natural incentive for the tech industry to want to play down its own energy consumption and carbon emissions, to give the impression that “there’s nothing to see here”.
Although most studies on this topic officially state that they have no conflicts of interest, many of those same studies are funded by tech companies or conducted by research teams at big tech companies. That is not inherently a problem, but I have been witness to conversations in private that have led me to be cautious here, and I believe it’s important that we keep our eyes open.
Similarly, I have heard suggestions that some studies reporting very high emissions may have been funded or influenced by industries that are being negatively impacted by digital transformation, and which are therefore keen to slow down the digitalisation of society by suggesting that there are little or no environmental benefits. God help us!
Erring on the side of caution
Like anything in life, we ultimately have to ask ourselves why we even care about quantifying the energy consumption (and carbon emissions) of websites.
Surely there is one reason above all others – we want to minimise the impact of our web projects in contributing to climate change.
With this in mind, it is helpful to contemplate the worst case scenarios if we as an industry underestimate or overestimate our emissions.
If we underestimate our emissions, we might conclude that there is no problem to be solved, ignore the issue entirely, and continue to build a web that threatens our chances of keeping global warming under 2°C.
If we overestimate our emissions, the worst case scenario is that we build even faster, more efficient web services, save resources, and accelerate the transition to a zero carbon future.
It’s worth keeping this in mind when selecting the data that we use to calculate energy consumption and carbon emissions of the web services that we create and use.
If you are interested in learning more about web sustainability, subscribe to our Curiously Green newsletter below, visit WebsiteCarbon.com, read the Sustainable Web Manifesto, or get in touch to start a conversation 🙂
Tom is our resident sustainable design nut. He co-founded Wholegrain Digital in 2007 with the aim of helping good people benefit from good design.
The UK Electricity Costs of Home Broadband ISP Routers Compared
The SuperHub 3 itself supports 802.11ac WiFi (2.4GHz at up to 300Mbps and 5GHz at up to 1300Mbps), 4 x Gigabit Ethernet ports, 2 x Telephone ports (these seem to be disabled) and we should also mention that it uses a 1.2GHz Intel Atom CPU (odd choice for a router).
This is also the only DOCSIS 3.0 cable router we’ve looked at and that, combined with the CPU choice, means power consumption may be a bit higher than others (note: cable broadband connections tend to go a lot faster than VDSL2 and usually need a little more power).
Power Consumption (Manufacturer Figures)
Minimum / IDLE = 12 Watts
Maximum / LOAD = 15.3 Watts
The results suggest that the SuperHub 3 will cost between £16.28 (IDLE) and £20.77 (LOAD) per year to run, which would make this the most expensive router of our study. Sadly this is one of the routers that we couldn’t test directly and that’s a frustration because we would have liked to know how power usage is impacted by switching the router into modem-only mode (perhaps one of our readers might be able to answer that?).
* Sky Broadband (Sky Q Hub)
At present Sky’s latest router is normally only supplied to their “fibre broadband” subscribers (note: it also supports ADSL) and annoyingly it also offers just 2 x Gigabit Ethernet ports, although on the flip side it does support Powerline (AV1.1) networking but this only works with other Sky Q Kit. Elsewhere its 802.11ac WiFi claims to offer a good top speed of up to 1600Mbps via 2.4GHz and 5GHz.
Otherwise the spec sheet is quite sparse, although in benchmarks we’ve seen that the Sky Q Hub often comes close to BT’s new SmartHub router and that’s a top-of-the-line piece of kit, at least in terms of bundled ISP hardware.
Power Consumption (Our Figures)
Minimum / IDLE = 8.5 Watts
Maximum / LOAD = 11 Watts
The official documentation claims that the Sky Q Hub’s “Network Standby” mode uses “less than 12W of power” within 20 minutes of no use, although our own real-world testing figures, displayed above, found that it only used around 8.5 Watts when IDLE and kept to 11 Watts under LOAD.
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The results suggest that the Sky Q Hub will cost between £11.53 (IDLE) and £14.93 (LOAD) per year to run, which puts it somewhere between the arguably superior BT SmartHub and Virgin Media’s power hungry SuperHub 3. Take note that you can save some power by enabling its Ethernet Energy Efficiency mode, but that didn’t seem to make much of a difference and is only applicable for wired connections.
* Plusnet (Hub Zero)
Zero by name, zero by nature. This router is best described as a bog standard piece of kit, which is based off the Sagemcom 2704N and similar to Sky’s SR101 in terms of core features (i.e. it only supports ADSL2+ broadband, 802.11n WiFi @ 2.4GHz and 4 x 100Mbps Ethernet ports).
Plusnet didn’t initially provide any official IDLE and LOAD figures for the Zero, but they did state that it will use an “average” of around 3.4-3.8 Watts. The technical manual for the original unbranded router separately suggests that it’s unlikely to gobble more than 7 Watts at absolute maximum. Since then Plusnet has provided us with the following figures.
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Power Consumption (ISP Figures)
Minimum / IDLE = 4 Watts
Maximum / LOAD = 7 Watts
Luckily we did know somebody on Plusnet and were able to identify that the routers IDLE power consumption is just a shade above 3 Watts, but sadly we were unable to test it under multi-device LOAD. Never the less the results appear to be roughly in keeping with the SR101 that was tested earlier and so a LOAD of around 5 Watts seems more plausible than Plusnet’s 7 Watts, but we assume the manufacture was able to push it further than we could.
In other words, the Hub Zero should cost you between around £4.09 (3 Watts IDLE) and £9.49 (7 Watts LOAD) per year to run, which given our measured IDLE consumption would make it one of the cheapest router’s to run.
* Plusnet (Hub One)
The Hub One is essentially a re-branded and slightly cut-down version of BT’s HomeHub 5A router and as such it’s best to assume that the power consumption figures are likely to be almost identical to BT’s kit, as examined earlier. However Plusnet did provide is with
Power Consumption (ISP Figures)
Minimum / IDLE = 7.16 Watts
Maximum / LOAD = 9.69 Watts
We note that Plusnet’s figures are higher than what we recorded for the HomeHub 5, but then we saw the same with their Hub Zero data. This could of course be down to all sorts of things, such as slight differences in testing or the sensitivity of our basic testing equipment. On top of that Plusnet’s doesn’t use identical firmware.
Using Plusnet’s figures we’d estimate that the Hub One should cost you between around £9.71 (IDLE) and £13.14 (LOAD) per year to run.
Conclusion
None of the above devices are likely to burn a hole in your pocket (electricity bill), at least not to an extent that is any greater than occasionally charging your Smartphone or leaving a single energy-saving light switched-on 24/7. A lot of modern LED light bulbs only use around 4-8 Watts, which is similar to many of the routers we examined.
Generally almost all of the routers being bundled by major ISPs are likely to cost no more than around £15 per year to run and that’s assuming you a) leave them switched-on 24/7 as intended and, b) make simultaneous use of multiple connected devices (computers, smartphones etc.). In reality the impact upon your pocket will probably be less, partly depending upon your energy tariff.
The one exception was Virgin Media’s SuperHub 3 which, if the official figures are to be believed, appears to be quite a power hungry beast and may set you back an extra +£5 per year over the others. Now that’s hardly worth worrying about, but it’s still a consideration for the unemployed and those on much lower incomes.
Furthermore Internet providers will often bundle cheaper and lower spec hardware with their broadband packages. By comparison you could buy a superior third-party router, although in our experience most of these will still keep their usage below the level of Virgin’s SuperHub 3 under LOAD.
Going forwards the future generation of G.fast and DOCSIS 3.1 equipped wireless broadband routers, which are also likely to support new standards like 802.11ax WiFi and 801.22ad WiGig, will be more demanding and so we may revisit the issue of power consumption again in a few years’ time.
Otherwise we’d take the above figures (both ours and those from the ISPs / manufacturers’) with a pinch of salt as power consumption is difficult to measure and there’s plenty of margin for error with all of the potential variations involved, but these ball park figures should at least give you a rough indication of the cost.
In an ideal world the router manufactures’ would all be making their power consumption levels clear, instead of merely posting the PSU’s maximum rating.
Intelligent Wi-Fi
Intelligent Wi-Fi provides four features that aim to improve consumers’ Wi-Fi experience:
- Network Bearer Switching
- Auto Wi-Fi
- Suspicious Hotspot Detection
- Enhanced Power Saving
Intelligent Wi-Fi is the new brand name of the existing “Adaptive Wi-Fi” which had been applied to models older than Galaxy S10 (e.g. Galaxy S9 or older models). It has been improved by adding a new feature such as Suspicious Network Detection and also enhancing existing features such as Network Bearer Switching.
We care about speed, and we also care about your spending. With more than ten years of experience in Wi-Fi technology on mobile devices, Samsung has been conducting extensive research on features that actually improve the Wi-Fi experience for consumers. As a result, we have identified four main consumer concerns, and developed solutions with artificial intelligence and a mix of other existing technologies:
1. Network Bearer Switching
1-1. Enhanced Gray Area Detection
Consumers are often frustrated when they lose Internet connection or experience very low quality of service (QoS), even when the Wi-Fi signal seems to be strong. This is one of the most commonly reported consumer concerns over using Wi-Fi, the so-called “˜gray areas’.
In 2015, Samsung introduced an industry first — a technology that enables gray area detection and automatic network switching: Switch to Mobile Data (formerly referred to as “˜Smart Network Switch’). Gray areas can occur in a wide range of environments and in multiple forms, and Switch to Mobile Data is the solution for quickly switching from Wi-Fi to a mobile data network when a gray area is encountered.
This new technology, which has been applied to the Galaxy S10 and later models such as the Galaxy Note 10, detects gray areas based on a wider range of technologies available to us, including sensors and artificial intelligence. Sensors collect context information, and artificial intelligence engines then analyze this data to determine the presence of a gray area. Our upgraded version switches networks faster, particularly in the following circumstances:
- Areas with a sudden loss of connection, i.e. in elevators
- Unstable Wi-Fi on moving vehicles, including buses, trains, and subways
Our new software contributes to a significant reduction in Internet disconnections, as shown in the following figure.
1-2. Preference Tracking
Switch to Mobile Data is a technology that automatically switches from a mobile data network to Wi-Fi and vice versa. When we first launched this feature in 2015, it became popular among carriers and users, although some consumers were left unsatisfied. Since the switching criteria was previously based on network performance, it would switch to a mobile data network even when the consumer would have preferred to stay on Wi-Fi.
We have been experimenting with various ways of improving network switching to better fit user preference and network usage patterns. As a result, we have created a new switching technology based on reinforcement learning, which automatically switches networks based on both network quality and user preference.
Our new connection manager will not activate until it has accumulated enough data on user preference. On average, it will track your network usage preferences for approximately 10 days.
*As a result of Galaxy S9 Internal User Trial (2018), it took an average of 9.9 days to determine user preferences.
When it has gathered enough data to determine which of the above types you fall into, it will then initiate the automatic network switching scheme. For instance, if you prefer to conserve your data, the connection manager will remain on Wi-Fi as long as possible. On the contrary, if it detects that you prefer speed above all, it will attempt to switch to a mobile data network as soon as the Wi-Fi coverage begins to drop. If your preferences change over time, our new connection manager will continue to track your actions in order to accommodate your needs.
2. Auto Wi-Fi
People use Wi-Fi differently based on their location. In places where Wi-Fi is available, we turn on Wi-Fi to avoid being charged for mobile data. On the other hand, if Wi-Fi is always on, we are subjected to frequent, unwanted connections and higher power consumption. To solve this problem, we have introduced Auto Wi-Fi, which turns Wi-Fi on and off depending on your location. Auto Wi-Fi addresses these connectivity-related pain points.
Auto Wi-Fi pays close attention to your connection patterns and remembers your favorite networks. It turns your Wi-Fi on when a favorite network is available. When you leave the area and the network becomes unavailable, Auto Wi-Fi will automatically turn off your Wi-Fi.
Auto Wi-Fi uses geofencing (a virtual geological fence based on cellular stations) to detect a user’s location. In registered geofenced areas, “ENTER” and “EXIT” events will be triggered based on location changes detected via the user’s device. Since Auto Wi-Fi is only triggered when these events take place, the device does not have to constantly scan for location, thereby saving battery life. Samsung’s original cell-based geofencing technique allows users to use Auto Wi-Fi without turning GPS on. Samsung’s geofencing technique also incorporates learning algorithms which improves location accuracy over time as users continue to use it.
3. Suspicious Hotspot Detection
One of the most common forms of Wi-Fi attacks is through suspicious hotspots. These hotspots appear on the scan list, often with names (SSIDs) similar to other legitimate access points (APs). In actuality, these hotspots capture all transmitted packets or attempt to manipulate the packets to their advantage.
Offered in the Galaxy S10 and later models such as the Galaxy Note 10, Detect Suspicious Network is a client-based solution that has been designed to protect our customers from these attacks. The technology detects potentially suspicious hotspots by analyzing traffic patterns on a given network. Upon connecting to potentially suspicious APs, an instant warning message will pop up. Since our technology is based on real-time pattern analysis and not databases, it can also detect hotspots that may change their names continuously.
4. Enhanced Power Saving
Wi-Fi data usage on mobile devices has been growing at 67% CAGR, and almost 90% of mobile phone data is consumed over Wi-Fi. The increasing Wi-Fi traffic is generating demand for more advanced power saving techniques to increase battery life. Currently, power saving techniques attempt to reduce power consumption mainly by transferring large data files requiring high-speed throughput, such as movie and video files, through a broadband Wi-Fi network. This, however, may not be suitable for more widely used Wi-Fi applications such as VoIP, Web Browsing, video and audio streaming, live broadcasting, gaming, and SNS.
- This is because legacy techniques do not consider the real usage of throughput, latency, and bit rate required for various applications and services.
- To address this problem, intelligent Wi-Fi provides Enhanced Power Saving.
- Enhanced Power Saving techniques can reduce power consumption while using various data services through Wi-Fi network.
- It enables lower power consumption by changing the power saving scheme based on real-time traffic pattern analysis during use of various network applications over Wi-Fi.
- This technique is compatible with WFA-certified APs.
In order to minimize current power consumption, the Enhanced Power Saving technology keeps monitoring data traffic patterns and adaptively determines the optimal timing to enter power saving mode and when to wake up without degradation of service quality (i.e. throughput and latency performances). It offers an intelligent, real-time analysis of data transactions over Wi-Fi, including the bit rate, frequency and interval of Tx and Rx packets.
Enhanced Power Saving can reduce power consumption up to 50% — 75% for user applications that utilize a Wi-Fi network.