Saumitra Mohan Das

Performance comparison of scalable location services for geographic ad hoc routing

Geographic routing protocols allow stateless routing in mobile ad hoc networks (MANETs) by taking advantage of the location information of mobile nodes and thus are highly scalable. A central challenge in geographic routing protocols is the design of scalable distributed location services that track mobile node locations. A number of location services have been proposed, but little is known about the relative performance of these location services. In this paper, we perform a detailed performance comparison of three rendezvous-based location services that cover a range of design choices: a quorum-based protocol (XYLS) which disseminates each nodes location to O(/spl radic/N) nodes, a hierarchical protocol (GLS) which disseminates each nodes location to O(logN) nodes, and a geographic hashing based protocol (GHLS) which disseminates each nodes location to O(1) nodes. We present a quantitative model of protocol overheads for predicting the performance tradeoffs of the protocols for static networks. We then analyze the performance impact of mobility on these location services. Finally, we compare the performance of routing protocols equipped with the three location services with two topology-based routing protocols, AODV and DSR, for a wide range of network sizes. Our study demonstrates that when practical MANET sizes are considered, robustness to mobility and the constant factors matter more than the asymptotic costs of location service protocols. In particular, while GLS scales better asymptotically, GHLS is far simpler, transmits fewer control packets, and delivers more data packets than GLS when used with geographic routing in MANETs of sizes considered practical today and in the near future. Similarly, although XYLS scales worse asymptotically than GLS, it transmits fewer control packets and delivers more data packets than GLS in large mobile networks.

High-Throughput Multicast Routing Metrics in Wireless Mesh Networks

The stationary nature of nodes in a mesh network has shifted the main design goal of routing protocols from maintaining connectivity between source and destination nodes to finding high-throughput paths between them. In recent years, numerous link-quality-based routing metrics have been proposed for choosing high-throughput paths for unicast protocols. In this paper we study routing metrics for high-throughput tree or mesh construction in multicast protocols. We show that there is a fundamental difference between unicast and multicast routing in how data packets are transmitted at the link layer, and accordingly there is a difference in how the routing metrics for each of these primitives are designed. We adapt certain routing metrics for unicast for high-throughput multicast routing and propose news ones not previously used for high-throughput. We then study the performance improvement achieved by using different link-quality-based routing metrics via extensive simulation and experiments on a mesh network testbed, using ODMRP as a representative multicast protocol. Our testbed experiment results show that ODMRP enhanced with linkquality routing metrics can achieve up to 17.5% throughput improvement as compared to the original ODMRP.

Routing with a Markovian Metric to Promote Local Mixing

Routing protocols have traditionally been based on finding shortest paths under certain cost metrics. A conventional routing metric models the cost of a path as the sum of the costs on the constituting links. This paper introduces the concept of a Markovian metric, which models the cost of a path as the cost of the first hop plus the cost of the second hop conditioned on the first hop, and so on. The notion of the Markovian metric is fairly general. It is potentially applicable to scenarios where the cost of sending a packet (or a stream of packets) over a link may depend on the previous hop of the packet (or the stream). Such scenario arises, for instance, in a wireless mesh network equipped with local mixing, a recent link layer advance. This scenario is examined as a case study for the Markovian metric. The local mixing engine sits between the routing and MAC layers. It maintains information about the packets each neighbor has, and identifies opportunities to mix the outgoing packets via network coding to reduce the transmissions in the air. We use a Markovian metric to model the reduction of channel resource consumption due to local mixing. This leads to routing decisions that can better take advantage of local mixing. We have implemented a system that incorporates local mixing and source routing using a Markovian metric in Qualnet. The experimental results demonstrate significant throughput gain and resource saving.

Symmetrical Fairness in Infrastructure Access in Multi-hop Wireless Networks

In this paper, we study the problem of providing fairness in multi-hop wireless infrastructure access. In such networks, it is well known that the use of current media access and transport protocols can result in severe unfairness and even starvation for flows originated from different numbers of hops away from a wired infrastructure point or gateway. In this paper, we study a different type of fairness that exists in such networks - flows initiated by nodes that are similar numbers of hops away from the gateway can experience significant unfairness, and such unfairness exists even for perfectly symmetrical node distribution and channel conditions. We denote such fairness as symmetrical fairness. We first provide a framework to characterize and measure symmetrical fairness. We then perform an extensive set of simulation experiments to quantify the causes of symmetrical unfairness. Finally, we develop and study a distributed routing algorithm that significantly improves the symmetrical fairness

On-Device Mobile Phone Security Exploits Machine Learning

The authors present a novel approach to protecting mobile devices from malware that might leak private information or exploit vulnerabilities. The approach, which can also keep devices from connecting to malicious access points, uses learning techniques to statically analyze apps, analyze the behavior of apps at runtime, and monitor the way devices associate with Wi-Fi access points.