In standard IEEE 802.11 based systems, when the wireless client migrates away from the radio range of the currently associated access point (AP), network applications temporarily loose connectivity till the client is able to re-associate itself with a new AP. The delay that occurs during the break-off interval can vary from a few hundreds of microseconds to a few seconds. However, delay sensitive applications such as Voice over IP (VoIP) or streaming multimedia applications usually are unable to tolerate such long connectivity delays that fall beyond the range of 50 - 200 ms. This results in dropped calls or frozen video frames. In this paper we describe the design, implementation, and evaluation of a software based framework that facilitates seamless and transparent handoff between different APs in standard IEEE 802.11 based wireless local area networks (WLANs). Although different solutions are available in the literature that seek to address the handoff latency, most of them propose changes that are outside the purview of the current 802.11 standards. We have specifically kept such compatibility restrictions in mind and have devised a software based client side solution that is capable of reducing handoff delays to an average value of 20 ms. It is available as a driver update to the client and requires no additional support from the network. As part of our solution, we have successfully implemented and tested our proposed solution framework on Atheros AR5212 chipsets using the open source MadWifi driver.

In networks carrying large volume of traffic, accurate traffic characterization is necessary for understanding the dynamics and patterns of network resource usage. Previous approaches to flow characterization are based on random sampling of the packets (e.g., Cisco's NetFlow) or inferring characteristics solely based on long lived flows (LLFs) or on lossy data structures (e.g., bloom filters, hash tables). However, none of these approaches takes into account the heavy-tailed nature of the Internet traffic and separates the estimation algorithm from the flow measurement architecture.In this paper, we propose an alternate approach to traffic characterization by closely linking the flow measurement architecture with the estimation algorithm. Our measurement framework stores complete information related to short lived flows (SLFs) while collecting partial information related to LLFs. For real-time separation of LLFs and SLFs, we propose a novel algorithm based on typical sequences from information theory. The distribution (pdf) and sample space of the underlying traffic is estimated using the non-parametric Parzen window technique and likelihood function defined over the Coupon collector problem. We validate the accuracy and performance of our estimation technique using traffic traces from the internal LAN in our laboratory and from National Library for Applied Network Research (NLANR).

Emerging multi-rate wireless systems both cellular (cdma2000, high data rate (HDR)) and Wi-Fi like systems demand that the currently used channel estimation and scheduling techniques be revisited. Not only the channel estimation, prediction algorithms, and protocols need to be modified, the premise for evaluating their performance needs to be changed. Current techniques comprise of scheduling algorithms based on existing channel state. We recommend that for enhanced performance in terms of user satisfaction and system throughput not only channel state but also the demand rate needed for satisfying the user needs to be considered. In this paper, we devise new mechanisms to estimate the varying channel conditions using information theoretic techniques for multi-rate wireless systems. We utilized a non-parametric estimator of Renyi's entropy using the Parzen widowing technique to estimate the probability density function of the channel rate variation as experienced by every user in the system. Scheduling algorithms are proposed based on the channel conditions estimated by the proposed technique as well as the data rate needed for satisfying each user . The proposed mechanism ensures the highest number of satisfied users by maintaining the stipulated QoS requirements and fairness amonst users. We also demonstrate that maximizing the throughput does not necessarily result in maximizing the number of satisfied users. Results demonstrate that the proposed estimation techniques perform better than existing schemes both in terms of number of satisfied user and throughput.

Opportunistic scheduling algorithms are effective in exploiting channel variations and maximizing system throughput in multi-rate wireless networks. However, most scheduling algorithms ignore the per-user quality of service (QoS) requirements and try to allocate resources (i.e., the time slots) among multiple users. This leads to a phenomenon commonly referred to as the exposure problem wherein the algorithms fail to satisfy the minimum slot requirements of the users due to substitutability and complementarity requirement of user slots. To eliminate this exposure problem, we propose a novel scheduling algorithm based on two phase combinatorial reverse auction with the primary objective to maximize the number of satisfied users in the system. We also consider maximizing the system throughput as a secondary objective. In the proposed scheme, multiple users bid to acquire the required number of time slots, and the allocations are done to satisfy the two objectives in a sequential manner. We provide an approximate solution to the proposed scheduling problem which is a NP-complete problem. We prove that our proposed algorithm is (1 + log m) times the optimal solution, where m is the number of slots in a schedule cycle. We also present an extension to this algorithm which can support more satisfied users at the cost of additional complexity. Numerical results are provided to compare the proposed scheduling algorithms with other competitive schemes.

Opportunistic scheduling algorithms are effective in exploiting channel variations and maximizing system throughput in multi-rate wireless networks. However, most scheduling algorithms ignore the per-user quality of service (QoS) requirements and try to allocate resources (e.g., the time slots) among multiple users. This leads to a phenomenon commonly referred to as the exposure problem wherein the algorithms fail to satisfy the minimum slot requirements of the users due to substitutability and complementarity requirements of user slots. To eliminate this exposure problem, we propose a novel scheduling algorithm based on two-phase combinatorial reverse auction with the primary objective to maximize the number of satisfied users in the system. We also consider maximizing the system throughput as a secondary objective. In the proposed scheme, multiple users bid for the required number of time slots, and the allocations are done to satisfy the two objectives in a sequential manner. We provide an approximate solution to the proposed scheduling problem which is NP-complete. The proposed algorithm has an approximation ratio of (1 + logm) with respect to the optimal solution, where m is the number of slots in a schedule cycle. Simulation results are provided to compare the proposed scheduling algorithm with other competitive schemes.

This paper proposes a new measurement architecture and associated traffic estimation algorithm called FastFlow that uses the heavy-tailed nature of Internet traffic in order to distinguish packets belonging to short lived flows (SLFs) and long lived flows (LLFs). While complete information is stored for SLFs, only partial information related to LLFs is collected using systematic sampling. The absence of data points in LLFs is approximated using a likelihood function defined over the coupon collector problem and the distribution of underlying traffic estimated using the non-parametric Parzen window technique. We validate the performance of our approach using traffic traces collected from our lab and observe that the estimated statistics match the observed traces with high accuracy.

In this paper we study the causal behavior of Rayleigh fading wireless channel and a single queue system buffer using the finite state Markov chain (FSMC) and the finite buffer fluid flow model. In the process, we propose a state partitioning scheme for capturing the wireless channel realities based on the combined effect of level crossing rate (LCR), average fade duration (AFD) and the average value of the signal SNR observed at the output of the matched filter or correlation demodulator. Relevant system parameters captured using such a scheme is linked with the fluid flow traffic model to create a modulated Markov process (MMP) that is used to calculate the packet loss rate and effective bandwidth of the system.