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39
Data mules: Modeling a threetier architecture for sparse sensor networks
 IN IEEE SNPA WORKSHOP
, 2003
"... Abstract — This paper presents and analyzes an architecture that exploits the serendipitous movement of mobile agents in an environment to collect sensor data in sparse sensor networks. The mobile entities, called MULEs, pick up data from sensors when in close range, buffer it, and drop off the data ..."
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Cited by 474 (7 self)
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Abstract — This paper presents and analyzes an architecture that exploits the serendipitous movement of mobile agents in an environment to collect sensor data in sparse sensor networks. The mobile entities, called MULEs, pick up data from sensors when in close range, buffer it, and drop off the data to wired access points when in proximity. This leads to substantial power savings at the sensors as they only have to transmit over a short range. Detailed performance analysis is presented based on a simple model of the system incorporating key system variables such as number of MULEs, sensors and access points. The performance metrics observed are the data success rate (the fraction of generated data that reaches the access points) and the required buffer capacities on the sensors and the MULEs. The modeling along with simulation results can be used for further analysis and provide certain guidelines for deployment of such systems. I.
Exploiting Mobility for Energy Efficient Data Collection
 in Sensor Networks,‖ Mobile Networks and Applications
, 2006
"... Abstract. We analyze an architecture based on mobility to address the problem of energy efficient data collection in a sensor network. Our approach exploits mobile nodes present in the sensor field as forwarding agents. As a mobile node moves in close proximity to sensors, data is transferred to the ..."
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Cited by 106 (1 self)
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Abstract. We analyze an architecture based on mobility to address the problem of energy efficient data collection in a sensor network. Our approach exploits mobile nodes present in the sensor field as forwarding agents. As a mobile node moves in close proximity to sensors, data is transferred to the mobile node for later depositing at the destination. We present an analytical model to understand the key performance metrics such as data transfer, latency to the destination, and power. Parameters for our model include: sensor buffer size, data generation rate, radio characteristics, and mobility patterns of mobile nodes. Through simulation we verify our model and show that our approach can provide substantial savings in energy as compared to the traditional adhoc network approach.
Exploiting Sink Mobility for Maximizing Sensor Networks Lifetime
 Proceedings of the 38th Annual Hawaii International Conference on System Sciences
, 2005
"... Abstract—This paper explores the idea of exploiting the mobility of data collection points (sinks) for the purpose of increasing the lifetime of a wireless sensor network with energyconstrained nodes. We give a novel linear programming formulation for the joint problems of determining the movement ..."
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Cited by 101 (2 self)
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Abstract—This paper explores the idea of exploiting the mobility of data collection points (sinks) for the purpose of increasing the lifetime of a wireless sensor network with energyconstrained nodes. We give a novel linear programming formulation for the joint problems of determining the movement of the sink and the sojourn time at different points in the network that induce the maximum network lifetime. Differently from previous solutions, our objective function maximizes the overall network lifetime (here defined as the time till the first node “dies ” because of energy depletion) rather than minimizing the energy consumption at the nodes. For wireless sensor networks with up to 256 nodes our model produces sink movement patterns and sojourn times leading to a network lifetime up to almost five times that obtained with a static sink. Simulation results are performed to determine the distribution of the residual energy at the nodes over time. These results confirm that energy consumption varies with the current sink location, being the nodes more drained those in the proximity of the sink. Furthermore, the proposed solution for computing the sink movement results in a fair balancing of the energy depletion among the network nodes. I.
Random Walk for SelfStabilizing Group Communication in AdHoc Networks
, 2002
"... We introduce a selfstabilizing group communication system for adhoc networks. The system design is based on random walks of mobile agents. Three possible settings for modeling the location of the processors in the adhoc network are presented; slow location change, complete random change, and n ..."
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Cited by 79 (8 self)
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We introduce a selfstabilizing group communication system for adhoc networks. The system design is based on random walks of mobile agents. Three possible settings for modeling the location of the processors in the adhoc network are presented; slow location change, complete random change, and neighbors with probability. The group membership algorithm is based on collecting and distributing information by a mobile agent. The new techniques support group membership and multicast, and also support resource allocation.
Virtual mobile nodes for mobile ad hoc networks
 in DISC04
, 2004
"... Abstract. One of the most significant challenges introduced by mobile networks is coping with the unpredictable motion and the unreliable behavior of mobile nodes. In this paper, we define the Virtual Mobile Node Abstraction, which consists of robust virtual nodes that are both predictable and relia ..."
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Cited by 45 (18 self)
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Abstract. One of the most significant challenges introduced by mobile networks is coping with the unpredictable motion and the unreliable behavior of mobile nodes. In this paper, we define the Virtual Mobile Node Abstraction, which consists of robust virtual nodes that are both predictable and reliable. We present the Mobile Point Emulator, a new algorithm that implements the Virtual Mobile Node Abstraction. This algorithm replicates each virtual node at a constantly changing set of real nodes, modifying the set of replicas as the real nodes move in and out of the path of the virtual node. We show that the Mobile Point Emulator correctly implements a virtual mobile node, and that it is robust as long as the virtual node travels through wellpopulated areas of the network. The Virtual Mobile Node Abstraction significantly simplifies the design of efficient algorithms for highly dynamic mobile ad hoc networks. 1
Randomized PursuitEvasion with Local Visibility
 SIAM Journal on Discrete Mathematics
, 2006
"... We study the following pursuitevasion game: One or more hunters are seeking to capture an evading rabbit on a graph. At each round, the rabbit tries to gather information about the location of the hunters but it can see them only if they are located on adjacent nodes. We show that two hunters su#ce ..."
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Cited by 41 (2 self)
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We study the following pursuitevasion game: One or more hunters are seeking to capture an evading rabbit on a graph. At each round, the rabbit tries to gather information about the location of the hunters but it can see them only if they are located on adjacent nodes. We show that two hunters su#ce for catching rabbits with such local visibility with high probability. We distinguish between reactive rabbits who move only when a hunter is visible and general rabbits who can employ more sophisticated strategies. We present polynomial time algorithms that decide whether a graph G is hunterwin, that is, if a single hunter can capture a rabbit of either kind on G.
Randomized pursuitevasion in graphs
 Proceedings of the International Colloquium on Automata, Languages and Programming (ICALP
, 2002
"... We analyze a randomized pursuitevasion game on graphs. This game is played by two players, a hunter and a rabbit. Let G be any connected, undirected graph with n nodes. The game is played in rounds and in each round both the hunter and the rabbit are located at a node of the graph. Between rounds b ..."
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Cited by 39 (0 self)
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We analyze a randomized pursuitevasion game on graphs. This game is played by two players, a hunter and a rabbit. Let G be any connected, undirected graph with n nodes. The game is played in rounds and in each round both the hunter and the rabbit are located at a node of the graph. Between rounds both the hunter and the rabbit can stay at the current node or move to another node. The hunter is assumed to be restricted to the graph G: in every round, the hunter can move using at most one edge. For the rabbit we investigate two models: in one model the rabbit is restricted to the same graph as the hunter, and in the other model the rabbit is unrestricted, i.e., it can jump to an arbitrary node in every round. We say that the rabbit is caught as soon as hunter and rabbit are located at the same node in a round. The goal of the hunter is to catch the rabbit in as few rounds as possible, whereas the rabbit aims to maximize the number of rounds until it is caught. Given a randomized hunter strategy for G, the escape length for that strategy is the worst case expected number of rounds it takes the hunter to catch the rabbit, where the worst case is with regards to all (possibly randomized) rabbit strategies. Our main result is a hunter strategy for general graphs with an escape length of only O(n log(diam(G))) against restricted as well as unrestricted rabbits. This bound is close to optimal since Ω(n) is a trivial lower bound on the escape length in both models. Furthermore, we prove that our upper bound is optimal up to constant factors against unrestricted rabbits. 1
Efficient and Robust Protocols for Local Detection and
 Propagation in Smart Dust Networks”, in the Journal of Mobile Networks (MONET
, 2005
"... Abstract. Smart Dust is a set of a vast number of ultrasmall fully autonomous computing and communication devices, with very restricted energy and computing capabilities, that cooperate to quickly and efficiently accomplish a large sensing task. Smart Dust can be very useful in practice, i.e., in ..."
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Cited by 24 (11 self)
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Abstract. Smart Dust is a set of a vast number of ultrasmall fully autonomous computing and communication devices, with very restricted energy and computing capabilities, that cooperate to quickly and efficiently accomplish a large sensing task. Smart Dust can be very useful in practice, i.e., in the local detection of a remote crucial event and the propagation of data reporting its realization. In this work we make an effort towards the research on smart dust from an algorithmic point of view. We first provide a simple but realistic model for smart dust and present an interesting problem, which is how to propagate efficiently information on an event detected locally. Then we present various smart dust protocols for local detection and propagation that are simple enough to be implemented on real smart dust systems, and perform, under some simplifying assumptions, a rigorous average case analysis of their efficiency and energy consumption (and their interplay). This analysis leads to concrete results showing that our protocols are very efficient and robust. We also validate the analytical results by extensive experiments.
Randomized pursuitevasion with limited visibility
, 2003
"... We study the following pursuitevasion game: One or more hunters are seeking to capture an evading rabbit on a graph. At each round, the rabbit tries to gather information about the location of the hunters but it can see them only if they are located on adjacent nodes. We show that two hunters suffi ..."
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Cited by 20 (2 self)
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We study the following pursuitevasion game: One or more hunters are seeking to capture an evading rabbit on a graph. At each round, the rabbit tries to gather information about the location of the hunters but it can see them only if they are located on adjacent nodes. We show that two hunters suffice for catching rabbits with such local visibility with high probability. We distinguish between reactive rabbits who move only when a hunter is visible and general rabbits who can employ more sophisticated strategies. We present polynomial time algorithms that decide whether a graph G is hunterwin, that is, if a single hunter can capture a rabbit of either kind on G.
Analysis of link reversal routing algorithms for mobile ad hoc networks
 In SPAA, 2003
"... Link reversal algorithms provide a simple mechanism for routing in mobile ad hoc networks. These algorithms maintain routes to any particular destination in the network, even when the network topology changes frequently. In link reversal, a node reverses its incident links whenever it loses routes ..."
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Cited by 19 (2 self)
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Link reversal algorithms provide a simple mechanism for routing in mobile ad hoc networks. These algorithms maintain routes to any particular destination in the network, even when the network topology changes frequently. In link reversal, a node reverses its incident links whenever it loses routes to the destination. Link reversal algorithms have been studied experimentally and have been used in practical routing algorithms, including TORA [8]. This paper presents the rst formal performance analysis of link reversal algorithms. We study these algorithms in terms of work (number of node reversals) and the time needed until the network stabilizes to a state in which all the routes are reestablished. We focus on the full reversal algorithm and the partial reversal algorithm, both due to Gafni and Berstekas [5]; the rst algorithm is simpler, while the latter has been found to be more ecient for typical cases. Our results are as follows: (1) The full reversal algorithm requires O(n2) work and time, where n is the number of nodes which have lost the routes to the destination. (2) The partial reversal algorithm requires O(n a + n2) work and time, where a is a nonnegative integer which depends on the state of the network. This bound is tight in the worst case, for any a. (3) There are networks such that for every deterministic link reversal algorithm, there are initial states which require requires (n2) work and time to stabilize. Therefore, surprisingly, the full reversal algorithm is asymptotically optimal in the worst case, while the partial reversal algorithm is not, since a can grow arbitrarily large.