People often seek information by asking other people even when they have access to vast reservoirs of information such as the Internet and libraries. This is because people are great sources of unique information, especially that which is location-specific, community-specific and time-specific. Social networking is effective because this type of information is often not easily available anywhere else. In this paper, we conceive a wireless virtual social network which mimics the way people seek information via social networking. PeopleNet is a simple, scalable and low-cost architecture for efficient information search in a distributed manner. It uses the infrastructure to propagate queries of a given type to users in specific geographic locations, called bazaars. Within each bazaar, the query is further propagated between neighboring nodes via peer-to-peer connectivity until it finds a matching

In this paper we consider the problem of searching for a node or an object (i.e., piece of data, file, etc.) in a large network. Applications of this problem include searching for a destination node in a mobile ad hoc network, querying for a piece of desired data in a wireless sensor network, and searching for a shared file in an unstructured peer-to-peer network. We limit our attention in this study to the class of controlled flooding search strategies where query/search packets are broadcast and propagated in the network until a preset TTL (time-to-live) value carried in the packet expires. Every unsuccessful search attempt results in an increased TTL value (i.e., larger search area) and the same process is repeated. The primary goal of this study is to derive search strategies (i.e., sequences of TTL values) that will minimize the cost of such searches associated with packet transmissions. The main results of this paper are as follows. When the probability distribution of the location of the object is known a priori, we present a dynamic programming formulation with which optimal search strategies can be derived that minimize the expected search cost. We also derive the necessary and sufficient conditions for two very commonly used search strategies to be optimal. When the probability distribution of the location of the object is not known a priori and the object is to minimize the worst-case search cost, we show that the best strategies are randomized strategies, i.e., successive TTL values are chosen from certain probability distributions rather than deterministic values. We show that given any deterministic TTL sequence, there exists a randomized version that has a lower worst-case expected search cost. We also derive an asymptotically (as the network size increases) optimal strategy within a class of randomized strategies.

In unstructured peer-to-peer networks, controlled flooding aims at locating an item at the minimum message cost. Dynamic querying is a new controlled flooding technique. While it is implemented in some peer-to-peer networks, little is known about its undesirable behavior and little is known about its general usefulness in unstructured peerto -peer networks. This paper describes the first evaluation and analysis of such techniques, and proposes novel techniques to improve them. We make three contributions. First, we find the current dynamic querying design is flawed. Although it is advantageous over the expanding ring algorithm in terms of search cost, it is much less attractive in terms of peer perceived latency, and its strict constraints on network connectivity prevent it from being widely adopted. Second, we propose an enhanced flooding technique which requires the search cost close to the minimum, reduces the search latency by more than four times, and loosens the constraints on the network connectivity. Thus, we make such techniques useful for the general unstructured peer-to-peer networks. Third, we show that our proposal requires only minor modifications to the existing search mechanisms and can be incrementally deployed in peer-to-peer networks.

Abstract — In this paper we consider the problem of searching for a node or an object (i.e., piece of data, file, etc.) in a large network. Applications of this problem include searching for a destination node in a mobile ad hoc network, querying for a piece of desired data in a wireless sensor network, and searching for a shared file in an unstructured peer-to-peer network. We consider the class of controlled flooding search strategies where query/search packets are broadcast and propagated in the network until a preset TTL (time-to-live) value carried in the packet expires. Every unsuccessful search attempt, signified by a timeout at the origin of the search, results in an increased TTL value (i.e., larger search area) and the same process is repeated until the object is found. The primary goal of this study is to find search strategies (i.e., sequences of TTL values) that will minimize the cost of such searches associated with packet transmissions. Assuming that the probability distribution of the object location is not known a priori, we derive search strategies that minimize the search cost in the worst-case, via a performance measure in the form of the competitive ratio between the average search cost of a strategy and that of an omniscient observer. This ratio is shown in prior work to be asymptotically (as the network size grows to infinity) lower bounded by 4 among all deterministic search strategies. In this paper we show that by using randomized strategies (i.e., successive TTL values are chosen from certain probability distributions rather than deterministic values), this ratio is asymptotically lower bounded by e. We derive an optimal strategy that achieves this lower bound, and discuss its performance under other criteria. We further introduce a class of randomized strategies that are sub-optimal but potentially more useful in practice. Index Terms — Query and search, TTL, controlled flooding search, wireless networks, randomized strategy, best worst-case performance, competitive ratio I.

Abstract. We present a comparative mathematical analysis of two important distinct approaches to hybrid push-pull querying in wireless sensor networks: structured hash-based data-centric storage (DCS) and the unstructured comb-needle (CN) rendezvous mechanism. Our analysis yields several interesting insights. For ALL-type queries pertaining to information about all events corresponding to a given attribute, we examine the conditions under which the two approaches outperform each other in terms of the average query and event rates. For the case of ANYtype queries where it is sufficient to obtain information from any one of the desired events for a given attribute, we propose and analyze a modified sequential comb-needle technique (SCN) to compare with DCS. We find that DCS generally performs better than CN/SCN for high query rates and low event rates, while CN/SCN perform better for high event rates. Surprisingly, for the cases of ALL-type aggregated queries and ANY-type queries, we find that there exist “magic number ” event rate thresholds, independent of network size or query probability, which dictate the choice of querying protocol. While our analysis is based on a single-sink square-grid deployment, we believe the insights can be generalized to random deployments. 1

In this paper we consider the problem of searching for a node or an object (i.e., piece of data, file, etc.) in a large wireless network. We consider the class of controlled flooding search strategies where query/search packets are broadcast and propagated in the network until a preset TTL (time-to-live) value carried in the packet expires. Every unsuccessful search attempt results in an increased TTL value (i.e., larger search area) and the same process is repeated. We derive search strategies that minimize the search cost in the worst-case, via a performance measure in the form of the competitive ratio between the average search cost of a strategy and that of an omniscient observer. This ratio is shown in prior work to be lower bounded by 4 among all deterministic search strategies. In this paper we show that by using randomized strategies this ratio is lower bounded by e. We derive an optimal strategy that achieves this lower bound, and discuss its performance under other performance criteria. 1

Abstract—In this paper, we investigate efficient strategies for supporting on-demand information dissemination and gathering in largescale wireless sensor networks. In particular, we propose a “comb-needle ” discovery support model resembling an ancient method: Use a comb to help find a needle in sand or a haystack. The model combines push and pull for information dissemination and gathering. The push component features data duplication in a linear neighborhood of each node. The pull component features a dynamic formation of an on-demand routing structure resembling a comb. The comb-needle model enables us to investigate the cost of a spectrum of push and pull combinations for supporting query and discovery in large-scale sensor networks. Our result shows that the optimal routing structure depends on the frequency of query occurrence and the spatial-temporal frequency of related events in the network. The benefit of balancing push and pull for information discovery is demonstrated. Index Terms—Information discovery, query, geographical routing, wireless sensor networks. Ç 1

In this paper we study the problem of searching for a node or a piece of data in an ad hoc network using random packet forwarding. In particular, we examine three different methods. The first is a random direction forwarding scheme where the query packet is forwarded along a randomly chosen direction (following an approximate straight line) till it either hits the destination node (the target) or the boundary. It bounces off the boundary in the latter case and the process continues till the target is found. In the second approach, in addition to query packet traversing the network, the target releases an advertisement packet that propagates along a randomly chosen direction so that all nodes visited by the advertisement packet obtain and store the target location information. In the third method the query packet is assumed to follow a random walk type of forwarding. Our primary interest is in comparing the average hitting time under these methods and the memory required to store location information. In particular, we show that under the random direction forwarding the target hitting time is Θ ( a2 b), where a and b denote the size/radii of the network and the target area, assumed to be circular in shape, respectively. The hitting time is Θ(a) with target advertisement, and Θ(a2 log a b) under the random walk type of forwarding. We further show that the target advertisement method achieves mean hitting time on the same order as greedy forwarding schemes with less memory requirement. We discuss practical implementation issues of these methods and provide simulation results on their performance under more realistic settings. walk

In this paper we consider the problem of query and search in a network, e.g., searching for a specific node or a piece of data. We limit our attention to the class of TTL (time-to-live) based controlled flooding search strategies where query/search packets are broadcast and relayed in the network until a preset TTL value carried in the packet expires. Every unsuccessful search attempt results in an increased TTL value (i.e., larger search area) and the same process is repeated. Every search attempt also incurs a cost (in terms of packet transmissions and receptions) and a delay (time till timeout or till the target is found). The primary goal is to derive search strategies (i.e., sequences of TTL values) that minimize a worst-case cost measure subject to a worst-case delay constraint. We present a constrained optimization framework and derive a class of optimal strategies, shown to be randomized strategies, and obtain their performance as a function of the delay constraint. We also use these results to discuss the trade-off between search cost and delay within the context of flooding search. Index Terms data query and search, TTL, controlled flooding search, wireless sensor and ad hoc networks, constrained optimization, randomized strategy, competitive analysis I.

Abstract — Flooding based strategies are conventionally employed to perform querying and broadcasting in sensor networks. 1 These schemes have low hop-delays of Θ ( ) to reach any node M(n) that is a unit distance away, where M(n) is the transmission range of any sensor node. However, in sensor networks with large radio ranges, flooding based broadcasting schemes cause many redundant transmissions leading to a broadcast storm problem. Many approaches have been proposed to mitigate the broadcast storm problem, where broadcast schemes employ some knowledge of the previous transmissions to reduce the extraneous transmissions. In this paper, we study the role of geographic information and state information (i.e. memory of previous messages or transmissions) in reducing the redundant