We present Greedy Perimeter Stateless Routing (GPSR), a novel routing protocol for wireless datagram networks that uses the positions of routers and a packet's destination to make packet forwarding decisions. GPSR makes greedy forwarding decisions using only information about a router's immediate neighbors in the network topology. When a packet reaches a region where greedy forwarding is impossible, the algorithm recovers by routing around the perimeter of the region. By keeping state only about the local topology, GPSR scales better in per-router state than shortest-path and ad-hoc routing protocols as the number of network destinations increases. Under mobility's frequent topology changes, GPSR can use local topology information to find correct new routes quickly. We describe the GPSR protocol, and use extensive simulation of mobile wireless networks to compare its performance with that of Dynamic Source Routing. Our simulations demonstrate GPSR's scalability on densely deployed wireless networks.

/pdf/gpsr-greedy-perimeter-stateless-routing-for-wireless-59gz21qf8y.pdf

GPSR: greedy perimeter stateless routing for wireless networks

https://www.cs.montana.edu/courses/csci566/Ref/CR-Survey.pdf

NeXt generation/dynamic spectrum access/cognitive radio wireless networks: a survey

Advances in processor, memory and radio technology will enable small and cheap nodes capable of sensing, communication and computation. Networks of such nodes can coordinate to perform distributed sensing of environmental phenomena. In this paper, we explore the directed diffusion paradigm for such coordination. Directed diffusion is datacentric in that all communication is for named data. All nodes in a directed diffusion-based network are application-aware. This enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in-network. We explore and evaluate the use of directed diffusion for a simple remote-surveillance sensor network.

/pdf/directed-diffusion-a-scalable-and-robust-communication-4tsx5vxsas.pdf

Directed diffusion: a scalable and robust communication paradigm for sensor networks

"A Survey of Mobility Models for Ad Hoc Network Research," Wireless Comm. & Mobile Computing (WCMC) : Special issue on Mobile Ad Hoc Networking : Research

In the performance evaluation of a protocol for an ad hoc network, the protocol should be tested under realistic conditions including, but not limited to, a sensible transmission range, limited buffer space for the storage of messages, representative data traffic models, and realistic movements of the mobile users (i.e., a mobility model). This paper is a survey of mobility models that are used in the simulations of ad hoc networks. We describe several mobility models that represent mobile nodes whose movements are independent of each other (i.e., entity mobility models) and several mobility models that represent mobile nodes whose movements are dependent on each other (i.e., group mobility models). The goal of this paper is to present a number of mobility models in order to offer researchers more informed choices when they are deciding upon a mobility model to use in their performance evaluations. Lastly, we present simulation results that illustrate the importance of choosing a mobility model in the simulation of an ad hoc network protocol. Specifically, we illustrate how the performance results of an ad hoc network protocol drastically change as a result of changing the mobility model simulated.

/pdf/a-survey-of-mobility-models-for-ad-hoc-network-research-ygkxat400x.pdf

A survey of mobility models for ad hoc network research

In distributed optimization and iterative consensus literature, a standard problem is for $N$ agents to minimize a function $f$ over a subset of Euclidean space, where the cost function is expressed as a sum $\sum f_i$. In this paper, we study the private distributed optimization (PDOP) problem with the additional requirement that the cost function of the individual agents should remain differentially private. The adversary attempts to infer information about the private cost functions from the messages that the agents exchange. Achieving differential privacy requires that any change of an individual's cost function only results in unsubstantial changes in the statistics of the messages. We propose a class of iterative algorithms for solving PDOP, which achieves differential privacy and convergence to the optimal value. Our analysis reveals the dependence of the achieved accuracy and the privacy levels on the the parameters of the algorithm. We observe that to achieve $\epsilon$-differential privacy the accuracy of the algorithm has the order of $O(\frac{1}{\epsilon^2})$.

Differentially Private Distributed Optimization

Ad hoc networks formed without the aid of any established infrastructure are typically multi-hop networks. Location dependent contention and "hidden terminal" problem make priority scheduling in multi-hop networks significantly different from that in wireless LANs. Most of the prior work related to priority scheduling addresses issues in wireless LANs. In this paper, priority scheduling in multi-hop networks is discussed. We propose a scheme using two narrow-band busy tone signals to ensure medium access for high priority source stations. The simulation results demonstrate the effectiveness of the proposed scheme.

Priority scheduling in wireless ad hoc networks

In distributed optimization and iterative consensus literature, a standard problem is for N agents to minimize a function f over a subset of Euclidean space, where the cost function is expressed as a sum Σ fi. In this paper, we study the private distributed optimization problem (PDOP) with the additional requirement that the cost function of the individual agents should remain differentially private. The adversary attempts to infer information about the private cost functions from the messages that the agents exchange. Achieving differential privacy requires that any change of an individual's cost function only results in unsubstantial changes in the statistics of the messages. We propose a class of iterative algorithms for solving PDOP, which achieves differential privacy and convergence to a common value. Our analysis reveals the dependence of the achieved accuracy and the privacy levels on the the parameters of the algorithm. We observe that to achieve e-differential privacy the accuracy of the algorithm has the order of O(1/e2).

Supporting high throughput is an important challenge in multihop mesh networks. Popular wireless LAN standards, such as IEEE 802.11, provision for multiple channels. In this article, we consider the use of multiple wireless channels to improve network throughput. Commercially available wireless network interfaces can typically operate over only one channel at a time. Due to cost and complexity constraints, the total number of interfaces at each host is expected to be smaller than the total channels available in the network. Under this scenario, several challenges need to be addressed before all the available channels can be fully utilized. In this article, we highlight the main challenges, and present two link-layer protocols for utilizing multiple channels. We also present a new abstraction layer that simplifies the implementation of new multichannel protocols in existing operating systems. This article demonstrates the feasibility of utilizing multiple channels, even if each host has fewer interfaces than the number of available channels

/pdf/multichannel-mesh-networks-challenges-and-protocols-36r4j8rvnh.pdf

Multichannel mesh networks: challenges and protocols

Checkpointing reduces loss of computation in the presence of failures. Two metrics characterize a checkpointing scheme: checkpoint overhead and checkpoint latency. The paper shows that a large increase in latency is acceptable if it is accompanied by a relatively small reduction in overhead. Also, for equidistant checkpoints, optimal checkpoint interval is shown to be typically independent of checkpoint latency.

Nitin H. Vaidya

Papers

Differentially Private Distributed Optimization

Priority scheduling in wireless ad hoc networks

Differentially Private Distributed Optimization

Multichannel mesh networks: challenges and protocols

Impact of checkpoint latency on overhead ratio of a checkpointing scheme