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

This report considers the problem of Byzantine fault-tolerance in synchronous parallelized learning that is founded on the parallelized stochastic gradient descent (parallelized-SGD) algorithm. The system comprises a master, and $n$ workers, where up to $f$ of the workers are Byzantine faulty. Byzantine workers need not follow the master's instructions correctly, and might send malicious incorrect (or faulty) information. The identity of the Byzantine workers remains fixed throughout the learning process, and is unknown a priori to the master. We propose two coding schemes, a deterministic scheme and a randomized scheme, for guaranteeing exact fault-tolerance if $2f < n$. The coding schemes use the concept of reactive redundancy for isolating Byzantine workers that eventually send faulty information. We note that the computation efficiencies of the schemes compare favorably with other (deterministic or randomized) coding schemes, for exact fault-tolerance.

Randomized Reactive Redundancy for Byzantine Fault-Tolerance in Parallelized Learning

Single fault-tolerant distributed shared memory using competitive update

Distributed shared memory systems maintain multiple replicas of the shared memory registers. Maintaining causal consistency in such systems has received significant attention in the past. However, much of the previous literature focuses on \em full replication wherein each replica stores a copy of all the registers in the shared memory. In this paper, we investigate causal consistency in partially replicated systems, wherein each replica may store only a subset of the shared data. To achieve causal consistency, it is necessary to ensure that, before an update is performed at any given replica, all causally preceding updates must also be performed. Achieving this goal requires some mechanism to track causal dependencies. In the context of full replication, this goal is often achieved using vector timestamps, with the number of vector elements being equal to the number of replicas. Building on the past work, this paper makes two key contributions: For a family of algorithms for maintaining causal consistency, we present necessary conditions on the metadata (which we refer as a \em timestamp ) that must be maintained by each replica. We present an algorithm for achieving causal consistency using a timestamp that matches one of the necessary conditions referred above, thus showing that the condition is necessary and sufficient both.

https://dl.acm.org/doi/pdf/10.1145/3212734.3212790

Brief Announcement: Partially Replicated Causally Consistent Shared Memory

In this paper, we present an efficient deterministic algorithm for consensus in presence of Byzantine failures. Our algorithm achieves consensus on an $L$-bit value with communication complexity $O(nL + n^4 L^{0.5} + n^6)$ bits, in a network consisting of $n$ processors with up to $t$ Byzantine failures, such that $t<n/3$. For large enough $L$, communication complexity of the proposed algorithm approaches $O(nL)$ bits. In other words, for large $L$, the communication complexity is linear in the number of processors in the network. This is an improvement over the work of Fitzi and Hirt (from PODC 2006), who proposed a probabilistically correct multi-valued Byzantine consensus algorithm with a similar complexity for large $L$. In contrast to the algorithm by Fitzi and Hirt, our algorithm is guaranteed to be always error-free. Our algorithm require no cryptographic technique, such as authentication, nor any secret sharing mechanism. To the best of our knowledge, we are the first to show that, for large $L$, error-free multi-valued Byzantine consensus on an $L$-bit value is achievable with $O(nL)$ bits of communication.

Error-Free Multi-Valued Consensus with Byzantine Failures

The goal of Byzantine Broadcast (BB) is to allow a set of fault-free nodes to agree on information that a source node wants to broadcast to them, in the presence of Byzantine faulty nodes. We consider design of efficient algorithms for BB in synchronous point-to-point networks, where the rate of transmission over each communication link is limited by its "link capacity". The throughput of a particular BB algorithm is defined as the average number of bits that can be reliably broadcast to all fault-free nodes per unit time using the algorithm without violating the link capacity constraints. The capacity of BB in a given network is then defined as the supremum of all achievable BB throughputs in the given network, over all possible BB algorithms. We develop NAB - a Network-Aware BB algorithm - for tolerating f faults in arbitrary point-to-point networks consisting of f ≥ 3f+1 nodes and having ≥ 2f+1 directed node disjoint paths from each node i to each node j. We also prove an upper bound on the capacity of BB, and conclude that NAB can achieve throughput at least 1/3 of the capacity. When the network satisfies an additional condition, NAB can achieve throughput at least 1/2 of the capacity. To the best of our knowledge, NAB is the first algorithm that can achieve a constant fraction of capacity of Byzantine Broadcast (BB) in general point-to-point networks.

/pdf/byzantine-broadcast-in-point-to-point-networks-using-local-sm9zryticf.pdf

Nitin H. Vaidya

Papers

Randomized Reactive Redundancy for Byzantine Fault-Tolerance in Parallelized Learning

Single fault-tolerant distributed shared memory using competitive update

Brief Announcement: Partially Replicated Causally Consistent Shared Memory

Error-Free Multi-Valued Consensus with Byzantine Failures

Byzantine broadcast in point-to-point networks using local linear coding