Officially the topic is “Radio Resource Management for Quality of Service in Heterogeneous Wireless Networks”. This is quite the mouthful, I know. Really what it boils down to is: Making various wireless technologies (Bluetooth, WiFi, WiMAX, 3G, 4G, … , etc) seamlessly work together. Many devices are capable of connecting to many of these radio access technologies (RATs), but often it is not seamless. What do I mean by this? Well suppose I am inside a university building, deep in the basement (where they tend to put CS students ) where there is no mobile reception (3G, 4G etc.). I start downloading a large file, or call someone via wifi. Now I want to walk to my car because it’s time to go home for the day. Many networks now are not able to handle this, and it is interrupted after you change networks. Furthermore, you often have to manually tell the device you want to leave one network and join another. Seamless means this should all happen without you noticing. This is the focus of my research.

The biggest problem that I am concerned with is called handoff or handover. This is when the switch between RATs occurs. Traditionally, this also occurs when a mobile device switches from one tower to another, and it usually involved predicting the motion of the device along with some other factors for Quality of Service (QoS). For a vertical handover, we may or may not need to predict motion. If the heterogeneous wireless network (HWN) is densely covered, many RATs are available throughout the coverage region (as opposed to a sparsely covered where a given location may have access to one technology at once). In a dense HWN, the problem becomes a multi-criteria question.

1. Which network is most economical for me to connect to?
2. Which configuration of (network, client) pairs is most profitable for the operator?
3. Which network is able to provide me with the required QoS?

More technical details to follow…

The Atria plan used large WiFi cells, with very expensive antennas (see the apartment building near University Plaza, which I believe was one of them).

Many other cities (much larger ones) have been very successful in providing blanket WiFi, using a completing different coverage model and cost model. The best example is San Francisco where a company named Meraki provides free wifi for over 100,000 people using their Mesh Router devices. These devices cost between $399 and $1500 each, which is still expensive, but likely much cheaper than anything used by Atria. These devices likely have much lower range and handle dense areas compared with the atria cells which seem to be designed for large areas, and require many people to subscribe to pay for their costs. The argument in the article that WiFi coverage in metropolitan areas is difficult seems like a terrible attitude to have for an area that has a reputation as a high tech leader. San Francisco likely has much greater challenges in this regard compared to our tiny city.

Example coverage map in San Francisco

Instead of using these expensive devices, much cheaper devices such as linksys wrt routers could be used. These routers support linux, and because of this much customization is possible such as mesh networking. While these devices are less reliable than the previous more expensive solutions, it may be a good way to at least get the network started cheaply. Additionally, areas which are not used by as many people could be covered with cheaper routers, while areas with more dense traffic may be covered by expensive ones.

The argument in the article that WiFi networks are unnecessary because of cellular networks is ludicrous! If that is the case, why are every smartphone and other device including WiFi radios in them? It’s because data on cellular networks is way to expensive. Any place where a device can get free or low cost WiFi should be used instead of the cellular network. One large problem with this at the moment, however is that it is not seamless to go from a cellular network to a wifi network. For example, it is often not possible to carry on a phone call while switching networks, or continue downloading or steaming without interruption. This will change with much of the research in heterogeneous networks.

One potential model that hasn’t been explored much is community wireless networks. In this case, devices could be provided to anyone willing to provide access to their own home network for the community. The incentive could be either donations from users, or a very small fee (2 – 5 dollars per month) which is distributed to providers. Additionally, anyone who provides a part of the network is able to get on free to other parts of the network. Of course, there’s nothing to stop the larger companies like Rogers and Bell from creating the same type of value added service. Since so many people already have wireless in their home using Rogers and Bell, they could create some type of login where you take your bandwidth quota with you and have access to anyone else’s network who is also participating. This way, you are not using their bandwidth cap (only their “speed” – which may introduce a whole other range of problems )

Abstract:
In wireless networks, conditions may change rapidly and unpredictably. Often wireless networks are not designed to adapt to these changing conditions and perform poorly when they become congested. The multi-hop broadcast nature of wireless mesh networks amplifies the problem of poor wireless performance. Mixed bias scheduling has previously been applied successfully to wireless mesh networks however, it still suffers from similar problems when conditions change rapidly. In this work we propose an adaptive mixed bias (AMB) algorithm which uses a tabu search approach to change based on delay and dropped packets in the network. The proposed scheduling approach consists of three important algorithms, namely, the tabu search algorithm, move generation, and utility function. The adaptive mixed bias approach is compared against IEEE 802.11 and the non-adaptive mixed bias approach. The performance is evaluated using the packet delivery ratio and average end-to-end delay metrics.

Here are the slides from the talk: BWCCA-NGWMN2010-final (pdf)
and here is the link to the pdf from the conference: Adaptive Mixed Bias Resource Allocation for Wireless Mesh Networks (pfd)

On top of the programming event, we have begun a speaker series at the University of Guelph. The goal here is to get enough people coming out so that we can eventually host an ACM distinguished speaker. Our first speaker was Dr. Dan Ashlock from the department of Mathematics at the University of Guelph. His talk was on video game content creation and was quite interesting. The slides for the talk are available on the ACM guelph website. The turnout was very good for the first talk and we hope it continues in the future. (for anyone interested in attending we also provide snacks!) There are more talks scheduled for throughout the semester from professors, students and industry speakers.

Lastly, we have launched a first version of our website: http://guelph.acm.org. The main purpose of this page is to display upcoming and past events. In the future we also intend on having a vote available for the distinguished speaker we are inviting to Guelph as well as the ability to vote on dates for upcoming events so that we can get the highest attendance possible.

Title: Radio Resource Management in Heterogeneous Wireless Networks

Abstract: Wireless networks are quickly becoming pervasive allowing users to stay connected anytime
anywhere. However, current implementations are not seamless. There are many different radio
access technologies (RATs), for example 802.11 (Wifi), 802.15 (Bluetooth, Zigbee), 802.16 (WiMAX)
and 3g cellular / mobile technologies which are not inter-operable. The aim of heterogeneous
wireless networking is to bridge the technological gap between the existing equipment and software.
When this occurs, the wireless spectrum may be used more efficiently and it is expected users will
have a better experience using the networks. Ideally, the devices should be able to select the best
RAT at a given moment in time based on some criteria, for example cost or capacity. A background
on the architecture of heterogeneous networks will be presented. Problems such as handover, radio
resource management and quality of service (QoS) will be discussed and a direction for further
study will be established.

Update: Here are the slides from the presentation for those who are interested:Research Day 2010 (pdf) – Jason Ernst

I have submitted the final draft of an adaptive scheduling algorithm for wireless networks to BWCCA conference in Japan. I intend to present my findings in November.

The ACM Group I am chair of is organizing a computer programming contest at Guelph for the fall semester. We are also planning to try to field a team for ACM ICPC* Update * We have been featured in the ACM student publication crossroads: http://tinyurl.com/25u5vxt.

A few days ago I found out I am the teaching assistant for CIS2030 at Guelph for the second time! I am excited to be back, and expect to do a good job since I am becoming familiar with the course.

Lastly, my thesis has been progressing slowly this semester. I am still in the stage where I am deciding where to focus my work. The general area I am exploring is heterogeneous wireless networks, however there are many problems and it is difficult to choose one to focus on. Since I worked in scheduling for my MSc. thesis, I intend to move in a bit different of a direction so that I expand my knowledge. (although I still may publish some more scheduling papers since I still have some ideas in that area)

While I don’t have a video for you to watch the entire presentation, I do have some pictures, and I’ll post the paper and slides I presented in case any one is interested. If you have any questions feel free to post on here or email me directly and I’ll be happy to answer any of them.

J.B. Ernst and M.K. Denko, “Cross-Layer Mixed Bias Scheduling for Wireless Mesh Networks,” in Proc. IEEE International Conference on Communications (ICC), Cape Town, South Africa, 2010.

Abstract—In this paper we propose a mixed bias approach which makes use of cross layer optimization. The cross-layer parameters are based on conditions in the network from multiple layers and are used to determine resource and time allocation for nodes in the network. Unlike existing proposals, we propose to bias against several parameters such as link quality and queue size in addition to node distance. We also propose a combined mixed bias approach which takes into account multiple parameters together. The scheme is evaluated using simulation experiments. The performance results are reported in this paper.

Download the paper (pdf)
Download the presentation slides (pdf)

The biggest lesson I’ve learned this semester is that the profs in undergrad were always right. Math is very important in CS. While it may not have been apparent at the time, (I always questioned the relevance of much of it since it was all presented very abstractly and disconnected from anything I was doing in undergrad) I now find some of my math skills to be my greatest weakness as a CS researcher. This is why this past semester I have gone out of my “comfort zone” and tried a couple of courses which I would generally shy away from. First is discrete optimization (Classifications of Optimization Problems, VRP, TSP, Bin-Packing, Cutting Stock, Simplex Method, PSO, Tabu Search, Branch & Cut etc). Second is a system performance and evaluation course (Queuing Theory, Markov models, Operational Analysis, etc). In each case, I have been pushed to the limits of what I know (and what I remember from my undergrad days). On the other hand, it has been easy to identify my weakest areas in my knowledge so that I can hopefully improve in them significantly before my quals in a couple of semesters. As an added benefit, perhaps because of the perspective I have gained as a grad student, I have started to realize the larger picture and see how many of the mathematical ideas can be applied to improve the state of the art in a particular problem area.

Lastly, I have been working at formulating my research and solidifying what it is I am working towards with this PhD. The very general description is “heterogeneous wireless networks”. Within this area, I am interested in particular type of heterogeneous wireless network. It is made up of a wireless mesh network as the backbone. Instead of traditional client nodes such as laptops, pdas etc, the clients are actually other types of wireless networks, for example: 802.11 WLAN, RFID, 3g Mobile etc. There are many problems when this type of network is considered. One is the gateway placement problem (where to place gateways between the types of networks, how many etc.). Also in each sub-network, there may be a particular set of parameters which cause the sub-network to perform very well. However when the sub-networks are joined, this set of parameters may not be optimal for the entire network as a whole. The problem then is how to optimize these sets of parameters with respect to the entire network.