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Hector is an Energy efficient Treebased Optimized Routing protocol for wireless networks
"... This paper considers the problem of designing power efficient routing with guaranteed delivery for sensor networks with known distances between neighbors but unknown geographic locations. We propose HECTOR, a hybrid energy efficient treebased optimized routing protocol, based on two sets of virtual ..."
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This paper considers the problem of designing power efficient routing with guaranteed delivery for sensor networks with known distances between neighbors but unknown geographic locations. We propose HECTOR, a hybrid energy efficient treebased optimized routing protocol, based on two sets of virtual coordinates. One set is based on rooted tree coordinates, and the other is based on hop distances toward several landmarks. In our algorithm, the node currently holding the packet will forward it to its neighbor that optimizes ratio of power cost over distance progress with landmark coordinates, among nodes that reduce landmark coordinates and do not increase tree coordinates. If such a node does not exist then forwarding is made to the neighbor that reduces tree based distance and optimizes power cost over tree distance progress ratio. Our simulations show the superiority of our algorithm over existing alternatives while guaranteeing delivery, and only up to 30 % additional power compared to centralized shortest weighted path algorithm.
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"... Hopbyhop data aggregation is a very important technique used to reduce the communication overhead and energy expenditure of sensor nodes during the process of data collection in a wireless sensor network (WSN). However, the unattended nature of WSNs calls for data aggregation techniques to be secu ..."
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Hopbyhop data aggregation is a very important technique used to reduce the communication overhead and energy expenditure of sensor nodes during the process of data collection in a wireless sensor network (WSN). However, the unattended nature of WSNs calls for data aggregation techniques to be secure. Indeed, sensor nodes can be compromised to mislead the base station (BS) by injecting bogus data into the network during both forwarding and aggregation of data. Moreover, data aggregation might increase the risk of confidentiality violations: If sensors close to the BS are corrupted, an adversary could easily access to the results of the ‘in network ’ computation performed by the WSN. Further, nodes can also fail due to random and nonmalicious causes (e.g., battery exhaustion), hence availability should be considered as well. In this paper we tackle the above issues that affect data aggregation techniques by proposing a mechanism that: (i) provides both confidentiality and integrity of the aggregated data so that for any compromised sensor in the WSN the information acquired could only reveal the readings performed by a small, constant number of neighboring sensors of the compromised one; (ii) detects bogus data injection attempts; (iii) provides high resilience to sensor failures. Our protocol is based on the concept of delayed aggregation and peer monitoring and requires local interactions only. Hence, it is highly scalable and introduces small overhead; detailed analysis supports our findings. Copyright © 2009 John Wiley & Sons, Ltd. KEY WORDS: wireless sensor networks; secure data aggregation; bogus data injection attack; node failure; peer monitoring; resilience 1.
Randomwalk Domination in Large Graphs
"... Abstract—We introduce and formulate two types of randomwalk domination problems in graphs motivated by a number of applications in practice (e.g., itemplacement problem in online social networks, Adsplacement problem in advertisement networks, and resourceplacement problem in P2P networks). Spec ..."
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Abstract—We introduce and formulate two types of randomwalk domination problems in graphs motivated by a number of applications in practice (e.g., itemplacement problem in online social networks, Adsplacement problem in advertisement networks, and resourceplacement problem in P2P networks). Specifically, given a graph G, the goal of the first type of randomwalk domination problem is to target k nodes such that the total hitting time of an Llength random walk starting from the remaining nodes to the targeted nodes is minimized. The second type of randomwalk domination problem is to find k nodes to maximize the expected number of nodes that hit any one targeted node through an Llength random walk. We prove that these problems are two special instances of the submodular set function maximization with cardinality constraint problem. To solve them effectively, we propose a dynamicprogramming (DP) based greedy algorithm which is with nearoptimal performance guarantee. The DPbased greedy algorithm, however, is not very efficient due to the expensive marginal gain evaluation. To further speed up the algorithm, we propose an approximate greedy algorithm with linear time complexity w.r.t. the graph size and also with nearoptimal performance guarantee. The approximate greedy algorithm is based on carefully designed random walk sampling and samplematerialization techniques. Extensive experiments demonstrate the effectiveness, efficiency and scalability of the proposed algorithms. I.
Approximating FaultTolerant Domination in General Graphs
"... In this paper we study the NPcomplete problem of finding small kdominating sets in general graphs, which allow k − 1 nodes to fail and still dominate the graph. The classic minimum dominating set problem is a special case with k = 1. We show that the approach of having at least k dominating nodes ..."
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In this paper we study the NPcomplete problem of finding small kdominating sets in general graphs, which allow k − 1 nodes to fail and still dominate the graph. The classic minimum dominating set problem is a special case with k = 1. We show that the approach of having at least k dominating nodes in the neighborhood of every node is not optimal. For each α> 1 it can give solutions k times larger α than a minimum kdominating set. We also study lower bounds on possible approximation ratios. We show that it is NPhard to approximate the minimum kdominating set problem with a factor better than (0.2267/k) ln(n/k). Furthermore, a result for special finite sums allows us to use a greedy approach for kdomination with an approximation ratio of ln( ∆ + k) + 1 < ln(∆) + 1.7, with ∆ being the maximum nodedegree. We also achieve an approximation ratio of ln(n) + 1.7 for hstep kdomination, where nodes do not need to be direct neighbors of dominating nodes, but can be h steps away. 1
Author manuscript, published in "N/P" Hector is an Energy effiCient Treebased Optimized Routing protocol
"... based on node hop count from a set of landmarks to obtain a virtual position. This approach is easy to implement and performances are interesting in terms of stretch factor and energy efficiency for some of the algorithms cited above [11]. However, packet delivery is not guaranteed even if a route b ..."
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based on node hop count from a set of landmarks to obtain a virtual position. This approach is easy to implement and performances are interesting in terms of stretch factor and energy efficiency for some of the algorithms cited above [11]. However, packet delivery is not guaranteed even if a route between the source and the destination exists. Indeed, several nodes may hold the same virtual coordinates and label unicity is required for guaranteeing delivery. The authors of [6] propose an alternative approach. In LTP [6], labels are assigned to nodes by building a tree through a depthfirst search on the network. Each node is assigned a label depending on its position in the tree. The routing paths are embedded in the labels. LTP guarantees the delivery but is not energy aware and may provide paths with a high stretch factor. In this paper, we focus on designing an energyaware and scalable routing protocol which guarantees delivery for sensor networks where nodes are not aware of any positioning information. We introduce HECTOR, a Hybrid EnergyeffiCient Treebased Optimized Routing protocol. HECTOR builds two sets of virtual coordinates: (i) virtual coordinates similar to the ones built in VCost, i.e. based on a node hop count distances to landmarks and (ii) a set of labels like in LTP. The first set of virtual coordinates allows HECTOR to find a greedy path in the forwarding direction of the destination. The second set of labels prevents HECTOR from reaching a dead end and the routing from failing. Based on these two sets of coordinates, a node holding a packet chooses its neighbor to forward the message in a CostoverProgress (COP [27]) fashion to save energy. The COP looks for nodes in the forwarding direction (here based on virtual coordinates or/and labels) and selects the one which minimizes the cost of transmission to this node over the progress made towards the destination.