Ameesh Pandya

Lossy source coding of multiple Gaussian sources: m-helper problem

We consider the network information theoretic problem of finding the rate distortion bound when multiple correlated Gaussian sources are present. One of these is the source of interest but some side information from other sources is also transmitted to help reduce the distortion in the reproduction of the first source. The other sources are treated as helpers and are also coded. Special cases of this problem have been solved before, such as when the reproduction is lossless, when the sources are conditionally independent given one of them, or when the number of helpers is limited to one. We consider a generalized version and show that the previously derived expressions fall out as special cases of our bound. Our results can be directly utilized by designers to choose not only how many of the available sources should actually be communicated but also which sources have the highest potential to reduce the distortion.

QoS in ad hoc networks

For mobile ad-hoc networks, nodes must balance a variety of tasks including sensing and communications relays. Mobile nodes might thus change location or trajectory for sensing purposes, subject to constraints on disruption of network QoS. This paper maximizes the non-communication application QoS (node motion to facilitate sensing) with communication QoS constraints (packet delay, etc.). It also gives the formulation for maximizing the throughput for the newly formed links at the new position of the node, taking link capacities into consideration. Each link is shared by multiple streams of traffic from different QoS classes, and each stream traverses many links. Although these formulations are non-linear, they can be posed as geometric programs, which can be solved efficiently. We also propose a heuristic to implement the above algorithm in ad hoc networks.

Quality of Service in Wireless Sensor Networks

This chapter contains sections titled: Introduction Fidelity and Resource Tradeoffs in Flat Networks Fidelity in Heterogeneous Networks Data Integrity in Sensor Networks Conclusions Exercises Bibliography

Spatial fidelity and estimation in sensor networks

We consider spatial fidelity in sensor networks and show that many problems in such networks are not well defined without it. The sensor networks gathering data on multiple point sources, or a distributed source such as bandlimited or nonbandlimited field are considered. The field could either be deterministic or random. For these networks, we derive the conditions leading to cooperation between the power constrained sensor nodes for the data fusion purposes and analyze the sensor density given spatial fidelity constraints and node cooperation. We also propose heuristics for sampling density for distributed sensor networks.

Bounds on achievable rates for cooperative channel coding

We address the problem of cooperative coding among pairs of transmitters and corresponding pairs of receivers. This may be used, for example, to overcome gaps in a multihop networks. We derive upper bounds on the achievable rates for several cooperation scenarios. We show that transmitter cooperation provides significantly more improvement in rate than receiver cooperation. Based on the theoretical results, we also discuss the possible practical implementation techniques.

Chapter 14. Quality of Service in Wireless Sensor Networks

This chapter contains sections titled: Introduction Fidelity and Resource Tradeoffs in Flat Networks Fidelity in Heterogeneous Networks Data Integrity in Sensor Networks Conclusions Exercises Bibliography