Probabilistic flooding (parameterized by a forwarding probability) has frequently been considered in the past, as a means of limiting the large message overhead associated with traditional (full) flooding approaches that are used to disseminate globally information in unstructured peer-topeer and other networks. A key challenge in using probabilistic flooding is the determination of the forwarding probability so that global network outreach is achieved while keeping the message overhead as low as possible. By showing that a probabilistic flooding network generated by applying probabilistic flooding to a connected random graph network can be bounded by properly parameterized random graph networks and invoking random graph theory results, bounds on the value of the forwarding probability are derived guaranteeing global network outreach with high probability, while significantly reducing the message overhead. Bounds on the average number of messages – as well as asymptotic expressions- and on the average time required to complete network outreach are also derived, illustrating the benefits of the properly parameterized probabilistic flooding scheme. 1.

Abstract—Flooding is well known to be the “fastest ” way to propagate information throughout a network (achieve 100% coverage), at the expense of typically unacceptably large messageforwarding overhead; when the overhead is controlled or limited, then the achieved coverage is reduced. A Single Random Walker (SRW) is another popular mechanism for information dissemination that is very “slow ” compared to flooding but utilizes less overhead and can potentially achieve better coverage than flooding. The latter may be attributed to the better “stretching” properties (ability to visit further away network regions) of the SRW and is particularly observed if coverage is defined as the set of network nodes which are at most L hops away from a network node which received the information (notion of L-coverage, L ≥ 0). Randomly Replicated Random Walkers (RRRWs) are considered

Matchmaking is the process of introducing two or more agents to each other. Current matchmaking techniques are unidirectional and fail to address large-scale and highly dynamic systems with time constraints. We propose a new distributed technique which scales well, and still maintains relatively low matchmaking time and communication overhead. Our technique introduces very low storage and computational overhead to the agents. We suggest using a matching cache which can take advantage of the multidirectional nature of the matchmaking problem. We empirically evaluate the proposed technique on bilateral matchmaking and show that it outperforms the existing techniques.

Abstract Service advertisement is a key design issue in modern dynamic and largescale networking environments such as unstructured peer-to-peer networks. The intrinsic capability of a single random walker of stretching the information dissemination over widely spread network areas (compared to flooding), is explored and exploited in this paper, along with the introduction of random walkers which can replicate themselves. Two replication policies are also introduced in this paper: the Topology Independent Policy that creates replicas according to an exponentially decreasing probability (creating more replicas at the beginning of the advertising process), and the Topology Dependent Policy in which replication decisions are based on some locally available topological information (aiming at creating replicas at the dense network areas). The discussion and the results in this paper reveal intrinsic comparative properties of flooding and the single random walker, as well as the advantages that the random walker replication can bring in improving the overhead, speed and coverage of the advertising process.

This thesis has two parts. The first part presents a hybrid approach to the coverge problem under dead reckoning errors. Coverage is a canonical robotics task, where single or multiple robots are given a target work area, and move about the area until every point in the area is visited by the robots. There are many efficient exact-motion coverage algorithms, that cannot be used in practice, because they assume accurate movements by the robot; unfortunately, real robots have navigational errors—called dead reckoning errors. A standard costly solution is to utilize a hybrid approach where an exact-motion algorithm is used on a robot that continuously localizes, so as to make course corrections. We propose a novel hybrid coverage algorithm, called TRIM SAIL. It takes as input an exact-movement algorithm, the coverage tool size, and a maximal dead-reckoning error bound. It optimizes use of the exact-movement algorithm, so as to execute its coverage plan while minimizing movement and localization costs. TRIM SAIL guarantees complete coverage, even under dead-reckoning errors. We present several variants of

Probabilistic flooding has been frequently considered as a suitable dissemination information approach for limiting the large message overhead associated with traditional (full) flooding approaches that are used to disseminate globally information in unstructured peer-to-peer and other networks. A key challenge in using probabilistic flooding is the determination of the forwarding probability so that global network outreach is achieved while keeping the message overhead as low as possible. In this paper, by showing that a probabilistic flooding network, generated by applying probabilistic flooding to a connected random graph network, can be (asymptotically) “bounded ” by properly parameterized random graph networks and by invoking random graph theory results, asymptotic values of the forwarding probability are derived guaranteeing (probabilistically) successful coverage, while significantly reducing the message overhead with respect to traditional flooding. Asymptotic expressions with respect to the average number of messages and the average time required to complete network coverage are also derived, illustrating the benefits of the properly parameterized probabilistic flooding scheme. Simulation results support the claims and expectations of the analytical results and reveal certain aspects of probabilistic flooding not covered by the analysis.

Abstract—Resource discovery is a challenging issue in unstructured peer-to-peer networks. Blind search approaches, including flooding and random walks, are the two typical algorithms used in such systems. Blind flooding is not scalable because of its high communication cost. On the other hand, the performance of random walks approaches largely depends on the random choice of walks. Some informed mechanisms use additional information, usually obtained from previous queries, for routing. Such approaches can reduce the traffic overhead but they limit the query coverage. Furthermore, they usually rely on complex protocols to maintain information at each peer. In this paper, we propose two schemes which can be used to improve the search performance in unstructured peer-to-peer networks. The first one is a simple caching mechanism based on resource descriptions. Peers that offer resources send periodic advertisement messages. These messages are stored into a cache and are used for routing requests. The second scheme is a dynamic Time-To-Live (TTL) enabling messages to break their horizon. Instead of decreasing the query TTL by 1 at each hop, it is decreased by a value v such as 0 < v < 1. Our aim is not only to redirect queries towards the right direction but also to stimulate them in order to reliably discover rare resources. We then propose a Dynamic Resource Discovery Protocol (DRDP) which uses the two previously described mechanisms. Through extensive simulations, we show that our approach achieves a high success rate while incurring a low search traffic. Keywords-Unstructured Peer-to-Peer networks; Resource discovery; Dynamic-TTL; P2P computing