This Innovative Practice Category Full Paper, analyses the effect of a game-based student response system to enhance experiential learning. The introductory engineering course offered for the freshman computer engineering cohort is designed with engineering practice at its core. Each week, the students spend two three-hour studio sessions experiencing engineering through hands-on practical applications followed by a two-hour reflective session. Among the two studios each week, only one is graded. The graded studios are designed with questions that supplement the practical application and spark reflective thinking. Consecutively, it is a good indicator of the effectiveness of the session. The ungraded studio sessions are designed the same as the graded ones but lack indicators of their efficacy for active implementation. To address this, an online game-based response system, Kahoot! is integrated with the ungraded studio sessions. Kahoot! combines game-based learning with the ease of a student response system that is useful in creating a stimulating environment and targets the competitive nature of students to supplement the ungraded studios. Kahoot! stands out from ordinary student response systems as it integrates a response time to the student's answers. Both qualitative and quantitative assessment results on the effectiveness of Kahoot! to augment experiential learning will be presented.

Unlike most existing underwater medium access control (MAC) protocols that use unidirectional data exchange, the recently proposed BiC-MAC protocol allows each sender-receiver pair to exchange multiple rounds of bidirectional-concurrent data transmissions. Via simulations, it was shown that BiC-MAC greatly improves channel utilization. In this paper, we propose a novel analytical framework based on an absorbing Markov chain to analyze the single-hop saturation throughput for slotted BiC-MAC, under error-prone channel conditions. Motivated by the insight that time-slotting loses its effects when inter-nodal delay is longer than packet transmission time, the presented results can serve as a close approximation for the original, unslotted BiC-MAC protocol. We model the protocol behavior for a sender-receiver pair that attempts to bidirectionally exchange their backlogged batch of packets. To compute saturation throughput from batch service time, we systematically derive both the state transition probabilities and the expected time spent in each Markov chain. Via comparison against the simulation results of unslotted and slotted BiC-MAC in both small and large topologies with channel errors, we show that the model approximates unslotted BiC-MAC reasonably well. We also present another approach that uses the actual inter-nodal delay information and offers even closer approximation to the throughput of unslotted BiC-MAC.

Fingerprint-based methods are widely adopted for indoor localization purpose because of their cost-effectiveness compared to other infrastructure-based positioning systems. However, the popular location fingerprint, Received Signal Strength (RSS), is observed to differ significantly across different devices' hardware even under the same wireless conditions. We derive analytically a robust location fingerprint definition, the Signal Strength Difference (SSD), and verify its performance experimentally using a number of different mobile devices with heterogeneous hardware. Our experiments have also considered both Wi-Fi and Bluetooth devices, as well as both Access-Point(AP)-based localization and Mobile-Node (MN)-assisted localization. We present the results of two well-known localization algorithms (K Nearest Neighbor and Bayesian Inference) when our proposed fingerprint is used, and demonstrate its robustness when the testing device differs from the training device. We also compare these SSD-based localization algorithms' performance against that of two other approaches in the literature that are designed to mitigate the effects of mobile node hardware variations, and show that SSD-based algorithms have better accuracy.

Existing approaches to indoor tracking have various limitations. Location-fingerprinting approaches are labor intensive and vulnerable to environmental changes. Trilateration approaches require at least three line-of-sight beacons for coverage at any point in the service area, which results in heavy infrastructure cost. Dead reckoning (DR) approaches rely on knowledge of the initial location and suffer from tracking error accumulation. Despite this, we adopt DR for location tracking because of the recent emergence of affordable hand-held devices equipped with low-cost DR-enabling sensors. In this paper, we propose an indoor pedestrian tracking system that comprises of a DR subsystem implemented on a mobile phone and a ranging subsystem with a sparse infrastructure. A particle-filter-based fusion scheme is applied to bound the accumulated tracking error by fusing DR with sparse range measurements. Experimental results show that the proposed system is able to track users much better than DR alone. The system is robust even when: 1) the initial user location is not available; 2) range updates are noisy; and 3) range updates are intermittent, both temporally and spatially.

Due to the widespread deployment and low cost, WLAN has drawn much attention for indoor localization. In this poster, an efficient indoor localization algorithm, which utilizes the WLAN received signal strength from each Access Point (AP), has been proposed. The algorithm is based on the Extreme Learning Machine (ELM), a Single layer Feed-forward neural Network (SLFN). It is competitive fast in offline learning and online localization. Also, compared with existing fingerprinting approach, it does not need the fingerprinting database in the online phase, which can substantially reduce the required storage space of the terminal devices.

The emergence of personal mobile device with low cost sensors, such as accelerometer and digital compass, has made dead-reckoning (DR) an attractive choice for indoor pedestrian tracking. In this paper, we propose a robust DR pedestrian tracking system on top of such commercially accessible sensor sets capable of DR. The proposed method exploits the fact that, multiple DR systems, carried by the same pedestrian, have stable relative displacements with respect to the center of motion, and therefore to each other. We first formulate the robust tracking task as a generalized maximum a posteriori sensor fusion problem, and then we narrow it to a simple computation procedure with certain assumptions. A prototype is implemented and evaluated with a benchmark system that collects ground truth efficiently and accurately. In a practical indoor testbed, the proposed scheme has exhibited robust tracking performance, with reduction in average tracking error up to 73.7%, compared to traditional DR tracking methods.

In this paper, we analyze the Cramer-Rao Lower Bound (CRLB) of localization using Signal Strength Difference (SSD) as location fingerprint. This analysis has a dual purpose. Firstly, the properties of the bound on localization error may help to design efficient localization algorithm. For example, utilizing one of the properties, we propose a way to define weights for a weighted K-Nearest Neighbor (K-NN) scheme which is shown to perform better than the K-NN algorithm. Secondly, it provides suggestions for a positioning system design by revealing error trends associated with the system deployment. In both cases, detailed analysis as well as experimental results are presented in order to support our claims.

Accurate indoor pedestrian tracking has wide applications in the healthcare, retail, and entertainment industries. However, existing approaches to indoor tracking have various limitations. For example, location-fingerprinting approaches are labor-intensive and vulnerable to environmental changes. Trilateration approaches require at least three Line-of-Sight (LoS) beacons to cover any point in the service area, which results in heavy infrastructure cost. Dead Reckoning (DR) approaches rely on knowledge of the initial user location and suffer from tracking error accumulation. Despite this, we adopt DR for location tracking because of the recent emergence of affordable hand-held devices equipped with low cost DR-enabling sensors. In this paper, we propose an indoor pedestrian tracking system which comprises a DR sub-system implemented on a mobile phone, and a ranging sub-system with a sparse infrastructure. A probabilistic fusion scheme is applied to bound the accumulated tracking error of DR when new range measurements are available from sparsely deployed beacons. Experimental results show that the proposed system is able to track users much better than DR alone, with reductions in average error by up to 71.9%. The system is robust and works well even when the initial user location is not available and range updates are intermittent. This highlights the potential of using sparse but reasonably accurate partial information to limit location tracking errors.

In most existing sender-initiated handshaking based underwater Media Access Control (MAC) protocols, only the initiating sender is allowed to transmit data packets to its intended receiver after the channel has been reserved; none of the potentially backlogged neighbors of the sender can transmit in the duration after the current handshake. Therefore, each of those neighbors must initiate their own handshakes, which incur additional overheads and potentially result in poor channel utilization. In this paper, we present a novel approach to increase the channel utilization by allowing a sender to invite its one-hop neighbors (appenders) to opportunistically transmit (append) their data packets. After the sender finishes transmitting its packets to its own receiver, it can immediately switch its role to receive the incoming appended data packets, which arrive in a packet train manner. This greatly reduces the relative proportion of time spent on control signaling. We refer to this MAC protocol as ROPA -- Reverse Opportunistic Packet Appending. From our extensive simulations and comparisons with existing protocols, we show that ROPA significantly increases the channel utilization and offers performance gains in terms of throughput and delay.

In this paper, we propose a fingerprint-based localization scheme that exploits the location dependency of the channel impulse response (CIR). We approximate the CIR by applying inverse Fourier transform to the receiver's channel estimation. The amplitudes of the approximated CIR (ACIR) vector are further transformed into the logarithmic scale to ensure that elements in the ACIR vector contribute fairly to the location estimation, which is accomplished through nonparametric kernel regression. As shown in our simulations, when both the number of access points and density of training locations are the same, our proposed scheme displays significant advantages in localization accuracy, compared to other fingerprint-based methods found in the literature. Moreover, absolute localization accuracy of the proposed scheme is shown to be resilient to the real time environmental changes caused by human bodies with random positions and orientations.

Due to the half-duplex property of the underwater acoustic channels, the classic stop-and-wait ARQ (SW-ARQ) and its variants are generally thought to be the only class of ARQ protocols that can be applied in underwater. When combined with the large propagation delay property of the underwater acoustic channels, the use of SW-ARQ and its variants makes the throughput performance of underwater acoustic communication systems very inefficient. In this paper, we propose a transmission scheme that takes advantage of the long propagation delay in underwater to enable the use of continuous ARQ protocols over underwater acoustic channels. Simulation results show that our proposed transmission scheme allows much higher throughput to be achieved than both the classic SW-ARQ and its variants, even when simple continuous ARQ protocols are used.

Unlike terrestrial wireless communication which uses radio waves, underwater communication relies on acoustic waves. The long latency and limited bandwidth pose great challenges in underwater Media Access Control (MAC) protocol design. As a result, terrestrial MAC protocols perform inefficiently when deployed directly in an underwater environment. In this paper, we examine how an existing asynchronous handshaking based protocol called Multiple Access Collision Avoidance (MACA) can be adapted for use in multi-hop underwater networks. Three areas of improvement are investigated, namely, the state transition rules, the packet forwarding strategy, and the backoff algorithm. Throughput performance is also evaluated through extensive simulation in multi-hop underwater networks. Due to its simplicity and throughput stability, our proposed MAC protocol can be adopted as a reference MAC protocol for underwater networks, with which a more sophisticated underwater MAC may benchmark its performance.

Location fingerprinting techniques generally make use of existing wireless network infrastructure. Consequently, the positions of the access points (APs), which constitute an integral part of a location system, will invariably be dictated by the network administrator's convenience regarding data communication. But the localization accuracy of fingerprint-based solutions is largely dependent on the APs' placements over the area. In this paper, we developed the idea of virtual access point (VAP), where one can have AP's functionality at a desired position for localization purpose, without physically placing an AP there. We argue that, placing VAPs at favorable positions helps to improve localization accuracy. VAP also serves the purpose of virtually increasing the number of APs over the localization area, which according to previous works should enhance the localization accuracy further. We test the feasibility of our VAP idea both analytically and experimentally. Finally, we present our results using a well-known localization algorithm, namely, k-nearest neighbor, when our VAP idea is implemented. The findings are quite encouraging, which report significant improvement in the localization accuracy.

Unlike the terrestrial wireless networks that utilize the radio channel, underwater networks use the acoustic channel, which poses research challenges in the medium access control (MAC) protocol design due to its low bandwidth and high propagation delay characteristics. Since most of the MAC protocols for wireless terrestrial networks have been designed with negligible propagation delay in mind, they generally perform poorly when applied directly in underwater acoustic networks, especially for the case of handshaking-based protocols. In this paper, we propose a MACA-based MAC protocol with packet train to multiple neighbors (MACA-MN). It improves the channel utilization by forming a train of packets destined for multiple neighbors during each round of handshake, which greatly reduces the relative proportion of time wasted due to the propagation delays of control packets. This approach also reduces the hidden terminal problem. Our simulations show that the MACA-MN is able to achieve much higher throughput than the MACA protocol.

Although there are many MAC protocols that have been proposed for terrestrial wireless networks with a wide variety of aspects, these protocols cannot be applied directly in underwater acoustic networks due to the channel's uniqueness of having low data rate and long propagation delay. In order to achieve a high throughput, both characteristics must be taken into account in the MAC design. We propose a random access MAC protocol for multi-hop underwater acoustic networks based on receiver reservation, which we shall call the "Receiverinitiated Packet Train" (RIPT) protocol. It is a handshakingbased protocol that addresses the channel's long propagation delay characteristic by utilizing receiver-initiated reservations, as well as by coordinating packets from multiple neighboring nodes to arrive in a packet train manner at the receiver. Our simulation results have confirmed that the RIPT protocol can achieve our goal of having high and stable throughput performance while maintaining low collision rate.

Unlike terrestrial networks that mainly rely on radio waves for communications, underwater networks utilize acoustic waves, which have comparatively lower loss and longer range in underwater environments. However, the use of acoustic waves pose a new research challenge in the networking area. While existing network schemes for terrestrial sensor networks are mainly designed for negligible propagation delay and high data rate, underwater acoustic communications are characterized by high propagation delay and low data rate. These terrestrial schemes, when directly applied to the underwater channel, will under-utilize its already limited capacity. We investigate how the underwater channel's throughput may be enhanced via medium access control (MAC) techniques that consider its unique characteristics. Specifically, we study the performance of Aloha-based protocols in underwater networks, and propose two enhanced schemes, namely, Aloha with collision avoidance (Aloha-CA), and Aloha with advance notification (Aloha-AN), which are capable of using the long propagation delays to their advantage. Simulation results have shown that both schemes can boost the throughput by reducing the number of collisions, and, for the case of Aloha-AN, also by significantly reducing the number of unproductive transmissions.

Indoor localization techniques using location fingerprints are gaining popularity because of their cost-effectiveness compared to other infrastructure-based location systems. However, their reported accuracy fall short of their counterparts. In this paper, we investigate many aspects of fingerprint-based location systems in order to enhance their accuracy. First, we derive analytically a robust location fingerprint definition, and then verify it experimentally as well. We also devise a way to facilitate under-trained location systems through simple linear regression technique. This technique reduces the training time and effort, and can be particularly useful when the surrounding or setup of the localization area changes. We further show experimentally that because of the positions of some access points or the environmental factors around them, their signal strength correlates nicely with distance. We argue that it would be more beneficial to give special consideration to these access points for location computation, owing to their ability to distinguish locations distinctly in signal space. The probability of encountering such access points will be even higher when we denote a location's signature using the signals of multiple wireless technologies collectively. We present the results of two wellknown localization algorithms (K-Nearest Neighbor and Bayesian Probabilistic Model) when the above factors are exploited, using Bluetooth and Wi-Fi signals. We have observed significant improvement in their accuracy when our ideas are implemented.

We provide an elaborate discussion on Bluetooth signal parameters with respect to localization, whereby we collectively designate all types of Bluetooth specification parameters that are related to signal strength - such as RSSI, Link Quality, Received and Transmit Power Level - as Bluetooth signal parameters. According to our analysis and experimental results, "RSSI" and "Transmit Power Level" turn out to be poor candidates for localization, while "Link Quality" has its limitations. On the other hand, "Received Power Level" correlates nicely with distance, which makes it the most desirable Bluetooth signal parameter to be used in location systems. We contend that it is vital to choose the appropriate signal parameter in Bluetooth location systems, and we expect our work to provide useful pointers in any future design of such systems. Existing systems can also benefit by adopting the appropriate Bluetooth signal parameter in their systems, and thereby, improve their location accuracy.

In cellular networks, an important call-level quality-of-service (QoS) issue is how to limit the probability of forced termination during handoffs. One solution is to predict the trajectory of mobile terminals so as to perform bandwidth reservation in advance. With the vision that future mobile devices are likely equipped with reasonably accurate positioning capability, we propose a novel mobility prediction technique that incorporates both mobile positioning information and road topology knowledge. We then develop an adaptive bandwidth reservation scheme that dynamically adjusts the reservation at each base station according to both incoming and outgoing hand-off predictions generated using our mobility prediction technique. We evaluate the performance of the scheme via simulations, along with six other schemes for comparison purposes. Results agree with intuition that schemes which incorporate more knowledge are able to achieve better reservation efficiency. Our scheme is shown to achieve the best efficiency among all realizable schemes simulated.

Current radio access networks are based on tree and star topologies, which have no inherent restorability properties. This paper proposes a heuristic topology enhancement method that adds redundant spans and upgrades existing infrastructure cost-effectively, in order to create partially meshed architectures that can provide the desired level of restorability against single span failures. The algorithm is tested using several different variants of restoration mechanisms. Results show that reasonably good solutions can be achieved in a short time scale. Finally, a service-oriented protection and restoration model is presented. Attributes are defined and used to create differentiated protection classes.

In the past, wireless network reliability issues have received limited attention. Previously proposed solutions have focused on protection against base station failures, which may require significantly more base stations than necessary. Actual data from wireless carriers suggest that problems in the backhaul are the uttermost reliability problems faced, and is therefore the main focus of this work. Current radio access networks are based on tree and star-like topologies, which have no inherent restorability properties. We propose a heuristic topology enhancement method that adds redundant spans and upgrades existing infrastructure cost-effectively, in order to create partially meshed architectures that could provide the desired level of restorability against single span failure scenarios. Both span and path restoration techniques are explored. The algorithm was tested using several different variants of restoration mechanisms. Results show that the proposed heuristic algorithm is able to achieve reasonably good solutions in a time scale that is several orders of magnitude faster than an optimization approach based on binary integer programming formulation.

In future wireless broadband networks, bandwidth demands could fluctuate abruptly due to movement of high data rate users. Dynamic bandwidth reservation plays a key rule in ensuring that mobile calls are not disrupted after they are admitted into the network. We propose a dynamic guard bandwidth scheme that adapts the amount of guard bandwidth in both wired and wireless links according to real-time handoff predictions made by individual mobile terminals, leveraged by the expected widespread availability of GPS-capable wireless devices in the near future. We describe procedures for approximating irregular handoff-request boundary, and for estimating a mobile's remaining time to handoff and target handoff cell. Guard bandwidth is adjusted on the fly only when a handoff is anticipated to occur shortly. The scheme is robust against modifications in handoff parameters, as well as changes in terrain and manmade features that could affect radio propagation. It also caters for heterogeneous bandwidth requirements. Admission control algorithms for both new calls and handoff calls are provided. Simulation results show that our scheme is able to meet specified forced termination probability under high offered load, and that resources are not reserved unnecessarily.

Amidst the rapid growth of wireless broadband networks, little attention has been paid to wireless network survivability issues. We propose a survivable wireless ATM network architecture that allows it to survive from a single base station failure condition, by redirecting a failure cell's traffic via its six neighboring cells. We present two failure-handling schemes for the proposed architecture. The first scheme is a bandwidth reservation (BR) scheme that is targeted at achieving high survivability by reserving bandwidths at appropriate locations in the network. The second scheme is a best-effort (BE) scheme that does not perform any reservation, and is targeted at achieving high bandwidth utilization. Simulation results show that the BR scheme achieves good survivability as expected. On the other hand, the BE scheme provides better utilization while having slightly lower average survivability. The decrease in average survivability for the BE scheme is not tremendous, largely due to its flexibility in the use of spare bandwidths from neighboring cells when a failure occurs. However, the BE scheme requires more frequent update messages between the switches in order to update each other about the amount of bandwidth that they could provide for failure-handling. We have also considered important issues, such as switchover time and data integrity, for our proposed schemes.

In this paper, we propose a generalised minimum queuing delay (GMQD) service discipline for high speed networks, mainly asynchronous transfer mode (ATM) networks. This proposed scheme is similar to service disciplines based on fair queuing, but instead of using only a single service rate for each session for its entire connection lifetime, multiple service rates are used. The service rate of any session at any point in time is computed efficiently based on the number of bits backlogged in the queues of the session and another imaginary reference session at that point in time. The main advantage of this scheme is that the queuing delays suffered by all the sessions connected to a single output node are minimised, leading to a smaller delay variation. In addition, this smaller delay variation also implies a smaller variance in the maximum queue length, thereby, reducing the possibility of buffer overflow.

Although neural networks have been applied for traffic and congestion control in ATM networks, most implementations use multi-layer perceptron (MLP) networks which are known to converge slowly. In this paper, we present a connection admission control (CAC) scheme which uses a modular neural network with fast learning ability to predict the cell loss ratio (CLR) at each switch in the network. A special type of OAM cell travels from the source node to the destination node and back in order to gather information at each switch. This information is used at the source to make CAC decisions such that quality of service (QoS) commitments are not violated. Experimental results which compare the performance of the proposed method with other CAC methods which use the peak cell rate (PCR), average cell rate (ACR) and equivalent bandwidth are presented.