Minho Shin

An empirical analysis of the IEEE 802.11 MAC layer handoff process

IEEE 802.11 based wireless networks have seen rapid growth and deployment in the recent years. Critical to the 802.11 MAC operation, is the handoff function which occurs when a mobile node moves its association from one access point to another. In this paper, we present an empirical study of this handoff process at the link layer, with a detailed breakup of the latency into various components. In particular, we show that a MAC layer function - probe is the primary contributor to the overall handoff latency. In our study, we observe that the latency is significant enough to affect the quality of service for many applications (or network connections). Further we find variations in the latency from one hand-off to another as well as with APs and STAs used from different vendors. Finally, we discuss optimizations on the probe phase which can potentially reduce the probe latency by as much as 98% (and a minimum of 12% in our experiments). Based on the study, we draw some guidelines for future handoff schemes.

Context caching using neighbor graphs for fast handoffs in a wireless network

User mobility in wireless data networks is increasing because of technological advances, and the desire for voice and multimedia applications. These applications, however, require fast handoffs between base stations to maintain the quality of the connections. Previous work on context transfer for fast handoffs has focused on reactive methods, i.e. the context transfer occurs after the mobile station has associated with the next base station or access router. In this paper, we describe the use of a novel and efficient data structure, neighbor graphs, which dynamically captures the mobility topology of a wireless network as a means for prepositioning the stations context ensuring that the stations context always remains one hop ahead. From experimental and simulation results, we find that the use of neighbor graphs reduces the layer 2 handoff latency due to reassociation by an order of magnitude from 15.37ms to 1.69ms, and that the effectiveness of the approach improves dramatically as user mobility increases.

Improving the latency of 802.11 hand-offs using neighbor graphs

The 802.11 IEEE Standard has enabled low cost and effective wireless LAN services (WLAN). With the sales and deployment of WLAN based networks exploding, many people believe that they will become the fourth generation cellular system (4G) or a major portion of it. However, the small cell size of WLAN creates frequent hand-offs for mobile users. If the latency of these hand-offs is high, as previous studies have shown, then the users of synchronous multimedia applications such as voice over IP (VoIP) will experience excessive jitter. The dominating factor in WLAN hand-offs has been shown to be the discovery of the candidate set of next access points. In this paper, we describe the use of a novel and efficient discovery method using neighbor graphs and non-overlap graphs. Our method reduces the total number of probed channels as well as the total time spent waiting on each channel. Our implementation results show that this approach reduces the overall probe time significantly when compared to other approaches. Furthermore, simulation results show that the effectiveness of our method improves as the number of non-overlapping channels increases, such as in the 5 GHz band used by the IEEE 802.11a standard.

Proactive key distribution using neighbor graphs

User mobility in wireless data networks is increasing because of technological advances, and the desire for voice and multimedia applications. These applications, however, require that handoffs between base stations (or access points) be fast to maintain the quality of the connections. In this article we introduce a novel data structure, the neighbor graph, that dynamically captures the mobility topology of a wireless network. We show how neighbor graphs can be utilized to obtain a 99 percent reduction in the authentication time of an IEEE 802.11 handoff (full EAP-TLS) by proactively distributing necessary key material one hop ahead of the mobile user. We also present a reactive method for fast authentication that requires only firmware changes to access points and hence can easily be deployed on existing wireless networks.

AnonySense: A system for anonymous opportunistic sensing

We describe AnonySense, a privacy-aware system for realizing pervasive applications based on collaborative, opportunistic sensing by personal mobile devices. AnonySense allows applications to submit sensing tasks to be distributed across participating mobile devices, later receiving verified, yet anonymized, sensor data reports back from the field, thus providing the first secure implementation of this participatory sensing model. We describe our security goals, threat model, and the architecture and protocols of AnonySense. We also describe how AnonySense can support extended security features that can be useful for different applications. We evaluate the security and feasibility of AnonySense through security analysis and prototype implementation. We show the feasibility of our approach through two plausible applications: a Wi-Fi rogue access point detector and a lost-object finder.

Robust routing in wireless ad hoc networks

A wireless ad hoc network is a collection of mobile nodes with no fixed infrastructure. The absence of a central authorization facility in dynamic and distributed environments requires collaboration among nodes. When a source searches for a route to a destination, an intermediate node can reply with its cached entry. To strengthen correctness of such a routing discovery process, we propose a method in which the intermediate node requests its next hop to send a confirmation message to the source. After receiving both a route reply and confirmation message, the source determines the validity of a path according to its policy. As a result, this strategy discourages malicious nodes from intercepting packets. Simulation results show a remarkable improvement in throughput (30% higher delivery ratio and 10% less data transmission overhead) with a moderate increase of control messages.

Distributed Channel Assignment for Multi-radio Wireless Networks

We consider the channel assignment problem for multihop wireless networks in which nodes have multiple interfaces. Given the number of interfaces at each node and available channels in the system, we find a feasible channel assignment to improve network performance. Even when routing is given, finding a channel assignment for optimal performance is NP-hard. We present the SAFE (skeleton assisted partition FrEe) channel assignment scheme, which uses randomized channel assignment in a distributed manner while maintaining network connectivity. SAFE can utilize all independent channels in the system while attempting to distribute edges sharing a particular channel evenly throughout the network. To handle topology change and incremental deployment better, SAFE decouples the channel assignment problem from routing. Our simulation results show that SAFE significantly improves network performance in terms of throughput and delay and is comparable to the best prior centralized scheme that jointly considers routing and channel assignment

Wireless Network Security and Interworking

A variety of wireless technologies have been standardized and commercialized, but no single technology is considered the best because of different coverage and bandwidth limitations. Thus, interworking between heterogeneous wireless networks is extremely important for ubiquitous and high-performance wireless communications. Security in interworking is a major challenge due to the vastly different security architectures used within each network. The goal of this paper is twofold. First, we provide a comprehensive discussion of security problems and current technologies in 3G and WLAN systems. Second, we provide introductory discussions about the security problems in interworking, the state-of-the-art solutions, and open problems.

Plug-n-trust: practical trusted sensing for mhealth

Mobile computing and sensing technologies present exciting opportunities for healthcare. Prescription wireless sensors worn by patients can automatically deliver medical data to care providers, dramatically improving their ability to diagnose, monitor, and manage a range of medical conditions. Using the mobile phones that patients already carry to provide connectivity between sensors and providers is essential to keeping costs low and deployments simple. Unfortunately, software-based attacks against phones are also on the rise, and successful attacks on privacy-sensitive and safety-critical applications can have significant consequences for patients. In this paper, we describe Plug-n-Trust (PnT), a novel approach to protecting both the confidentiality and integrity of safety-critical medical sensing and data processing on vulnerable mobile phones. With PnT, a plug-in smart card provides a trusted computing environment, keeping data safe even on a compromised mobile phone. By design, PnT is simple to use and deploy, while providing a flexible programming interface amenable to a wide range of applications. We describe our implementation, designed for Java-based smart cards and Android phones, in which we use a split-computation model with a novel path hashing technique to verify proper behavior without exposing confidential data. Our experimental evaluation demonstrates that PnT achieves its security goals while incurring acceptable overhead.

Challenges in Data Quality Assurance in Pervasive Health Monitoring Systems

Wearable, portable, and implantable medical sensors have ushered in a new paradigm for healthcare in which patients can take greater responsibility and caregivers can make well-informed, timely decisions. Health-monitoring systems built on such sensors have huge potential benefit to the quality of healthcare and quality of life for many people, such as patients with chronic medical conditions (such as blood-sugar sensors for diabetics), people seeking to change unhealthy behavior (such as losing weight or quitting smoking), or athletes wishing to monitor their condition and performance. To be effective, however, these systems must provide assurances about the quality of the sensor data. The sensors must be applied to the patient by a human, and the sensor data may be transported across multiple networks and devices before it is presented to the medical team. While no system can guarantee data quality, we anticipate that it will help for the system to annotate data with some measure of confidence. In this paper, we take a deeper look at potential health-monitoring usage scenarios and highlight research challenges required to ensure and assess quality of sensor data in health-monitoring systems.