Connectivity in Wireless Sensor Networks in the SINR Model
07 Aug 2012-pp 145-152
TL;DR: The NP-hardness of the MCA problem is shown, an algorithm that compute a channel assignment for 2-dimensional grid networks is proposed, and two constant-factor approximation algorithms that yield channel assignments in which the number of channels is bounded by O(Δ), where Δ is the maximum node degree of a network.
Abstract: In this paper, we study the Minimum Channel Assignment (MCA) problem for strong connectivity in wireless sensor networks in the physical model known as Signal-to-Interference-Noise-Ratio (SINR). The main issue is to compute a minimum channel assignment that yields a strongly-connected communication graph spanning all nodes such that the nodes assigned to the same channel can communicate without interference in the SINR model. The complexity measure is the number of channels, and our objective is to minimize it. We show the NP-hardness of the MCA problem, and propose an algorithm that compute a channel assignment for 2-dimensional grid networks. The algorithm produces an assignment with a constant number of channels for the network. We also propose two constant-factor approximation algorithms that yield channel assignments in which the number of channels is bounded by O(?), where ? is the maximum node degree of a network. We also study the performance of the algorithms through simulation.
Citations
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TL;DR: This paper proposes algorithms to build connected communication graphs with power-efficient links to be scheduled simultaneously in one time slot and proposes one greedy randomized constructive heuristic, two local search procedures, and three greedy randomized adaptive search procedures metaheuristics.
Abstract: A fundamental aspect in performance engineering of wireless sensor networks (WSN) is optimizing the set of links that can be concurrently activated to meet a given signal-to-interference-plus-noise ratio (SINR) threshold. The solution of this combinatorial problem is a key element in wireless link scheduling. Another key architectural goal in WSN is connectivity. The connectivity of sensor nodes is critical for WSN, as connected graphs can be used for both data collection and data dissemination. In this paper, we investigate the joint scheduling and connectivity problem in WSN assuming the SINR model. We propose algorithms to build connected communication graphs with power-efficient links to be scheduled simultaneously in one time slot. The algorithms aiming at minimizing the number of time slots needed to successfully schedule all the given links such that the nodes can communicate without interference in the SINR model. While power-efficient and interference-free schedules reduce energy consumption, minimization of the schedule length (shortest link scheduling) has the effect of maximizing network throughput. We propose one greedy randomized constructive heuristic, two local search procedures, and three greedy randomized adaptive search procedures metaheuristics. We report computational experiments comparing the effectiveness of the proposed algorithms. Our simulation also shows the trade-off between power consumption and schedule length and the results indicate that not only the overall performance of our algorithms, but also show that the total power and schedule length value of its solutions are better than the existing work.
19 citations
TL;DR: The geometric shape model is employed to evaluate the network connectivity probability of the WSN using the SA beam specifications and the analytical results agree with the simulation results by less than 4.7 % error in the average.
Abstract: In a wireless sensor network (WSN), after gathering information, tiny sensor nodes need to transmit data to a sink. It is important to guarantee that each node can communicate with a sink. Due to the multi-hop communication of WSNs, an essential condition for reliable transmission is completely connectivity of a network. Adaptive or smart antenna (SA) techniques in WSNs have been a topic of active research in recent years. These techniques have been shown to be effective with respect to decreasing energy consuming via specified regions which are formed by the SA beams. In this paper, we propose a probabilistic technique to determine the network connectivity probability of the SA integrated WSN. We employ the geometric shape model to evaluate the network connectivity probability of the WSN using the SA beam specifications. The sensor node density to satisfy the desired network connectivity is determined in terms of the beam-width of the antenna array and node transmission range. The analytical results agree with the simulation results by less than 4.7 % error in the average.
4 citations
Cites background from "Connectivity in Wireless Sensor Net..."
...The minimum channel assignment problem for strong connectivity in WSNs in the physical model known as signal to interference noise ratio is considered in [11]....
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02 Apr 2016-World Academy of Science, Engineering and Technology, International Journal of Computer and Information Engineering
TL;DR: This work investigates the problem with the bounded-sized message model in Wireless Sensor Networks, and introduces a constant factor approximation algorithm that is the first result of the data collection problem with bounded- sized model in both interference models.
Abstract: In this paper, we study the data collection problem in
Wireless Sensor Networks (WSNs) adopting the two interference
models: The graph model and the more realistic physical interference
model known as Signal-to-Interference-Noise-Ratio (SINR). The
main issue of the problem is to compute schedules with the minimum
number of timeslots, that is, to compute the minimum latency
schedules, such that data from every node can be collected without
any collision or interference to a sink node. While existing works
studied the problem with unit-sized and unbounded-sized message
models, we investigate the problem with the bounded-sized message
model, and introduce a constant factor approximation algorithm.
To the best known of our knowledge, our result is the first result
of the data collection problem with bounded-sized model in both
interference models.
2 citations
Additional excerpts
...Then τ = Ä P ·2π N(δ−α−1)(α−2) ä 1 α−2 is a lower bound for the shortest distance between r1 and s2 [28], and therefore d(r1, s2) ≥ τ ....
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TL;DR: The prediction model Support Vector Machine (SVM) is proposed to predict faulty nodes in wireless sensor network and add minimum number of relay nodes as compared to existing system to recover the network proactively from the faults and to continue working successfully.
Abstract: In Wireless Sensor Network (WSN), collected data will be faulty due to internal and external influences, such as environmental conditions, communication link failure, battery drain, etc. These are reduces the reliability of the WSN network. Faults may affect on quality of services (QoS), in WSN networks faults may produce incorrect data provided by sensor nodes or the network may make a misjudgment on nodes or the network and placing relay nodes to tolerate these faults, to improve Qos and reliability of the network. In this work, we propose the prediction model Support Vector Machine (SVM) to predict faulty nodes in wireless sensor network and add minimum number of relay nodes as compared to existing system to recover the network proactively from the faults and to continue working successfully.
References
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TL;DR: When n identical randomly located nodes, each capable of transmitting at W bits per second and using a fixed range, form a wireless network, the throughput /spl lambda/(n) obtainable by each node for a randomly chosen destination is /spl Theta/(W//spl radic/(nlogn)) bits persecond under a noninterference protocol.
Abstract: When n identical randomly located nodes, each capable of transmitting at W bits per second and using a fixed range, form a wireless network, the throughput /spl lambda/(n) obtainable by each node for a randomly chosen destination is /spl Theta/(W//spl radic/(nlogn)) bits per second under a noninterference protocol. If the nodes are optimally placed in a disk of unit area, traffic patterns are optimally assigned, and each transmission's range is optimally chosen, the bit-distance product that can be transported by the network per second is /spl Theta/(W/spl radic/An) bit-meters per second. Thus even under optimal circumstances, the throughput is only /spl Theta/(W//spl radic/n) bits per second for each node for a destination nonvanishingly far away. Similar results also hold under an alternate physical model where a required signal-to-interference ratio is specified for successful receptions. Fundamentally, it is the need for every node all over the domain to share whatever portion of the channel it is utilizing with nodes in its local neighborhood that is the reason for the constriction in capacity. Splitting the channel into several subchannels does not change any of the results. Some implications may be worth considering by designers. Since the throughput furnished to each user diminishes to zero as the number of users is increased, perhaps networks connecting smaller numbers of users, or featuring connections mostly with nearby neighbors, may be more likely to be find acceptance.
9,008 citations
"Connectivity in Wireless Sensor Net..." refers background or methods in this paper
...Thus, researchers have started focusing on the more realistic physical interference model which is known as the Signal-to-Interference-Noise-Ratio (SINR) model since its introduction by Gupta and Kumar in [2]....
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...We further make the assumption that a network represented as the directed graph G = (V,E), where E = {(u → v)|d(u, v) ≤ δ( P βN ) 1 α }, is strongly connected, and α > 2 [2]....
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...Our network model adopts the physical interference model (SINR) [2] where if a node u transmits its data to its receiver v with its power p(u), the received power at v is p(u) D(u,v)α , where α ∈ [2, 6] is the path loss exponent....
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TL;DR: The work of Dantzig, Fulkerson, Hoffman, Edmonds, Lawler and other pioneers on network flows, matching and matroids acquainted me with the elegant and efficient algorithms that were sometimes possible.
Abstract: Throughout the 1960s I worked on combinatorial optimization problems including logic circuit design with Paul Roth and assembly line balancing and the traveling salesman problem with Mike Held. These experiences made me aware that seemingly simple discrete optimization problems could hold the seeds of combinatorial explosions. The work of Dantzig, Fulkerson, Hoffman, Edmonds, Lawler and other pioneers on network flows, matching and matroids acquainted me with the elegant and efficient algorithms that were sometimes possible. Jack Edmonds’ papers and a few key discussions with him drew my attention to the crucial distinction between polynomial-time and superpolynomial-time solvability. I was also influenced by Jack’s emphasis on min-max theorems as a tool for fast verification of optimal solutions, which foreshadowed Steve Cook’s definition of the complexity class NP. Another influence was George Dantzig’s suggestion that integer programming could serve as a universal format for combinatorial optimization problems.
8,644 citations
01 Jan 2014
TL;DR: This chapter provides an overview of the fundamentals of algorithms and their links to self-organization, exploration, and exploitation.
Abstract: Algorithms are important tools for solving problems computationally. All computation involves algorithms, and the efficiency of an algorithm largely determines its usefulness. This chapter provides an overview of the fundamentals of algorithms and their links to self-organization, exploration, and exploitation. A brief history of recent nature-inspired algorithms for optimization is outlined in this chapter.
8,285 citations
07 Nov 2002
TL;DR: This work presents their own distributed algorithm that outperforms the existing algorithms for minimum CDS and establishes the /spl Omega/(n log n) lower bound on the message complexity of any distributed algorithm for nontrivial CDS, which is thus message-optimal.
Abstract: The connected dominating set (CDS) has been proposed as the virtual backbone or spine of a wireless ad hoc network. Three distributed approximation algorithms have been proposed in the literature for minimum CDS. We first reinvestigate their performances. None of these algorithms have constant approximation factors. Thus these algorithms can not guarantee to generate a CDS of small size. Their message complexities can be as high as O(n/sup 2/), and their time complexities may also be as large as O(n/sup 2/) and O(n/sup 3/). We then present our own distributed algorithm that outperforms the existing algorithms. This algorithm has an approximation factor of at most 8, O(n) time complexity and O(n log n) message complexity. By establishing the /spl Omega/(n log n) lower bound on the message complexity of any distributed algorithm for nontrivial CDS, our algorithm is thus message-optimal.
834 citations
"Connectivity in Wireless Sensor Net..." refers background or methods in this paper
...Then, as done in [10], a Maximal Independent Set (MIS) is found using an algorithm in [18] based on TBFS ....
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...Here, the MIS constructed by [18] guarantees that the distance between any pair of its complementary subsets is exactly two hops....
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09 Sep 2007
TL;DR: The first NP-completeness proofs in the geometric SINR model, which explicitly uses the fact that nodes are distributed in the Euclidean plane, are presented, which proves two problems to be NP-complete: Scheduling and One-Shot Scheduling.
Abstract: In this paper we study the problem of scheduling wireless links in the geometric SINR model, which explicitly uses the fact that nodes are distributed in the Euclidean plane. We present the first NP-completeness proofs in such a model. In particular, we prove two problems to be NP-complete: Scheduling and One-Shot Scheduling. The first problem consists in finding a minimum-length schedule for a given set of links. The second problem receives a weighted set of links as input and consists in finding a maximum-weight subset of links to be scheduled simultaneously in one shot. In addition to the complexity proofs, we devise an approximation algorithm for each problem.
430 citations
"Connectivity in Wireless Sensor Net..." refers background in this paper
...[7] proved that the scheduling problem in the geometric SINR model is NP-hard without power control, and later [14] extended the NP-hardness result for the case with power control assuming arbitrary power levels....
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...While these studies have been concerned with assigning minimum number of channels inducing a strongly connected communication graph, some other researchers have focused on other applications such as scheduling [6], [7], [5], [8], [9], data aggregation [10], [11], [12] and broadcast [13] in the SINR model....
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