Anand Kashyap

Performance Optimizations for Deploying VoIP Services in Mesh Networks

In the recent past, there has been a tremendous increase in the popularity of VoIP services as a result of huge growth in broadband access. The same voice-over-Internet protocol (VoIP) service poses new challenges when deployed over a wireless mesh network, while enabling users to make voice calls using WiFi phones. Packet losses and delay due to interference in a multiple-hop mesh network with limited capacity can significantly degrade the end-to-end VoIP call quality. In this work, we discuss the basic requirements for efficient deployment of VoIP services over a mesh network. We present and evaluate practical optimizing techniques that can enhance the network capacity, maintain the VoIP quality and handle user mobility efficiently. Extensive experiments conducted on a real testbed and ns-2 provide insights into the performance issues and demonstrate the level of improvement that can be obtained by the proposed techniques. Specifically, we find that packet aggregation along with header compression can increase the number of supported VoIP calls in a multihop network by 2-3 times. The proposed fast path switching is highly effective in maintaining the VoIP quality. Our fast handoff scheme achieves almost negligible disruption during calls to roaming clients

Predictive methods for improved vehicular WiFi access

With the proliferation of WiFi technology, many WiFi networks are accessible from vehicles on the road making vehicular WiFi access realistic. However, several challenges exist: long latency to establish connection to a WiFi access point (AP), lossy link performance, and frequent disconnections due to mobility. We argue that people drive on familiar routes frequently, and thus the mobility and connectivity related information along their drives can be predicted with good accuracy using historical information - such as GPS tracks with timestamps, RF fingerprints, and link and network-layer addresses of visible APs. We exploit such information to develop new handoff and data transfer strategies. The handoff strategy reduces the connection establishment latency and also uses pre-scripted handoffs triggered by change in vehicle location. The data transfer strategy speeds up download performance by using prefetching on the APs yet to be encountered. Experimental performance evaluation reveals that the predictability of mobility and connectivity is high enough to be useful in such protocols. In our experiments with a vehicular client accessing road-side APs, the handoff strategy improves download performance by roughly a factor of 2 relative to the state-of-the-art. The data transfer strategy further improves this performance by another factor of 2.5.

A measurement-based approach to modeling link capacity in 802.11-based wireless networks

We present a practical, measurement-based model that captures the effect of interference in 802.11-based wireless LAN or mesh networks. The goal is to model capacity of any given link in the presence of any given number of interferers in a deployed network, carrying any specified amount of offered load. Central to our modeling approach is a MAC-layer model for 802.11 that is fed by PHY-layer models for deferral and packet capture behaviors, which in turn are profiled based on measurements. The target network to be evaluated needs only O(N) measurement steps to gather metrics for individual links that seed the models. We provide two solution approaches - one based on direct simulation (slow, but accurate) and the other based on analytical methods (faster, but approximate). We present elaborate validation results for a 12 node 802.11b mesh network using upto 5 interfering transmissions. We demonstrate, using as comparison points three simpler modeling approaches, that the accuracy of our approach is much better, predicting link capacities with errors within 10% of the base channel datarate for about 90% of the cases.

Design and evaluation of iMesh: an infrastructure-mode wireless mesh network

We have designed and evaluated iMesh, an infrastructure-mode 802.11-based mesh network. IEEE 802.11 access points double as routers making the network architecture completely transparent to mobile clients, who view the network as a conventional wireless LAN. Layer-2 handoffs between access points trigger routing activities inside the network, which can be thought of as layer-3 handoffs. We describe the design rationale and a testbed implementation of iMesh. We present results related to the handoff performance. The results demonstrate excellent handoff performance, the overall latency varying between 50-100 ms, depending on different layer-2 techniques, even when a five-hop long route update is needed. Various performance measurements also demonstrate the clear superiority of a flat routing scheme relative to a more traditional, Mobile IP-like scheme to handle layer-3 handoff.

VoIP on Wireless Meshes: Models, Algorithms and Evaluation

We study the problem of supporting VoIP calls in a wireless mesh network. Specifically, we propose solutions for call admission control (CAC) and route selection for VoIP calls. Call admission decisions must evaluate how the capacity of the mesh network is utilized by the existing calls. We address this issue via a measurement-based modeling effort to model mutual interference between wireless links. The modeling approach evaluates whether capacity constraints (or, required QoS metrics) will be satisfied if a new call is admitted with a given route. Evaluations with a 6-node 802.11a testbed demonstrate excellent accuracy of the model and thus also the CAC performance. We address the issue of route selection by also using a modeling approach that considers models of transmission and interference ranges to develop a polynomial-time algorithm to search for feasible routes. This problem takes exponential time for wireless networks without such modeling. In addition to studying feasibility, we study several routing metrics such as shortest feasible path and maximum residual feasible path. Finally, we develop a new method for routing using call statistics that uses prior calling patterns to avoid potentially critical links. We evaluate the performance of these route selection techniques via extensive simulations and demonstrate the superiority of using max residual feasible path over simply shortest feasible path, and routing using call statistics over max residual feasible path.

An integrated approach for P2P file sharing on multi-hop wireless networks

P2P file sharing protocol and ad hoc wireless routing protocol share many intriguing similarities even though they are motivated on totally different basis. The goal of P2P file sharing system such as KaZaa is to locate a set of servers that contain a given file and disseminate it efficiently. The key problem of an ad hoc network routing protocol is to determine which route to take to reach a given remote host. P2P file sharing application on mobile ad hoc network (MANET) has gained more momentum as shown in the research of recent years. One natural way is to implement P2P application and ad hoc routing at different layers they belong to. In this paper, we argue that instead of stacking one on the top of the other, more work needs to be done to make both P2P file sharing protocol and MANET routing protocol interact with each other. We extract the commonalities of these two and design a common query/response framework on which ad hoc network routing and P2P file sharing are integrated seamlessly. The extensive experiments show that our strategy performs better than the layered approach in terms of traffic, average delay and packet delivery ratio.

Passive Measurement of Interference in WiFi Networks with Application in Misbehavior Detection

We present a tool to estimate the interference between nodes and links in a live wireless network by passive monitoring of wireless traffic. This tool does not require any controlled experiments, injection of probe traffic in the network, or even access to the network nodes. Our approach requires deploying multiple sniffers across the network to capture wireless traffic traces. These traces are then analyzed using a machine learning approach to infer the carrier-sense relationship between network nodes. This coupled with an estimation of collision probabilities helps us to deduce the interference relationships. We also demonstrate an important application of this tool-detection of selfish carrier-sense behavior. This is based on identifying any asymmetry in carrier-sense behavior between node pairs and finding multiple witnesses to raise confidence. We evaluate the effectiveness of the tool for both the applications using extensive experiments and simulation. Experimental and simulation results demonstrate that the proposed approach of estimating interference relations is significantly more accurate than simpler heuristics and quite competitive with active measurements. We also validate the approach in a real Wireless LAN environment. Evaluations using a real testbed as well as ns2 simulation studies demonstrate excellent detection ability of the selfish behavior. On the other hand, the metric of selfishness used to estimate selfish behavior matches closely with actual degree of selfishness observed.

Measurement-based approaches for accurate simulation of 802.11-based wireless networks

In this work, we address the issue of unrealistic simulations of wireless networks using a measurement-based approach. The idea is to use empirical modeling using measurement data as a mechanism to model physical layer behavior. We demonstrate the power of this approach for 802.11-based networks using ns2, a packet-level network simulator. Specifically, we develop two versions of the ns2 simulator that model the wireless physical layer with different levels of fidelity. In both versions, the deferral and reception model are built using measurements. For propagation modeling, one version uses direct measurements and the other uses an empirically derived model. In validation experiments with a 12-node mesh testbed, both these versions were found to be reasonably accurate (85 percentile errors within about 10% of the capacity) relative to regular simulations (85 percentile errors within 50% of capacity).

Deconstructing Interference Relations in WiFi Networks

Wireless interference is the major cause of degradation of capacity in 802.11 wireless networks. We present an approach to estimate the interference between nodes and links in a live wireless network by passive monitoring of wireless traffic. This does not require any controlled experiments, injection of probe traffic in the network, or even access to the network nodes. Our approach requires deploying multiple sniffers across the network to capture wireless traffic traces. These traces are then analyzed to infer the interference relations between nodes and links. We model the 802.11 MAC as a Hidden Markov Model (HMM), and use a machine learning approach to learn the state transition probabilities in this model using the observed trace. This coupled with an estimation of collision probabilities helps us to deduce the interference relationships. We show the effectiveness of this method against simpler heuristics, and also a profiling-based method that requires active measurements. Experimental results demonstrate that the proposed approach is significantly more accurate than heuristics and quite competitive with active measurements. We also validate the approach in a real WLAN environment.

Are You at Risk? Profiling Organizations and Individuals Subject to Targeted Attacks

Targeted attacks consist of sophisticated malware developed by attackers having the resources and motivation to research targets in depth. Although rare, such attacks are particularly difficult to defend against and can be extremely harmful. We show in this work that data relating to the profiles of organisations and individuals subject to targeted attacks is amenable to study using epidemiological techniques. Considering the taxonomy of Standard Industry Classification (SIC) codes, the organization sizes and the public profiles of individuals as potential risk factors, we design case-control studies to calculate odds ratios reflecting the degree of association between the identified risk factors and the receipt of targeted attack. We perform an experimental validation with a large corpus of targeted attacks blocked by a large security company’s mail scanning service during 2013–2014, revealing that certain industry sectors and larger organizations –as well as specific individual profiles – are statistically at elevated risk compared with others. Considering targeted attacks as akin to a public health issue and adapting techniques from epidemiology may allow the proactive identification of those at increased risk of attack. Our approach is a first step towards developing a predictive framework for the analysis of targeted threats, and may be leveraged for the development of cyber insurance schemes based on accurate risk assessments.