18 research outputs found
The Online Disjoint Set Cover Problem and its Applications
Given a universe of elements and a collection of subsets
of , the maximum disjoint set cover problem (DSCP) is to
partition into as many set covers as possible, where a set cover
is defined as a collection of subsets whose union is . We consider the
online DSCP, in which the subsets arrive one by one (possibly in an order
chosen by an adversary), and must be irrevocably assigned to some partition on
arrival with the objective of minimizing the competitive ratio. The competitive
ratio of an online DSCP algorithm is defined as the maximum ratio of the
number of disjoint set covers obtained by the optimal offline algorithm to the
number of disjoint set covers obtained by across all inputs. We propose an
online algorithm for solving the DSCP with competitive ratio . We then
show a lower bound of on the competitive ratio for any
online DSCP algorithm. The online disjoint set cover problem has wide ranging
applications in practice, including the online crowd-sourcing problem, the
online coverage lifetime maximization problem in wireless sensor networks, and
in online resource allocation problems.Comment: To appear in IEEE INFOCOM 201
Determining Distributions of Security Means for WSNs based on the Model of a Neighbourhood Watch
Neighbourhood watch is a concept that allows a community to distribute a
complex security task in between all members. Members of the community carry
out individual security tasks to contribute to the overall security of it. It
reduces the workload of a particular individual while securing all members and
allowing them to carry out a multitude of security tasks. Wireless sensor
networks (WSNs) are composed of resource-constraint independent battery driven
computers as nodes communicating wirelessly. Security in WSNs is essential.
Without sufficient security, an attacker is able to eavesdrop the
communication, tamper monitoring results or deny critical nodes providing their
service in a way to cut off larger network parts. The resource-constraint
nature of sensor nodes prevents them from running full-fledged security
protocols. Instead, it is necessary to assess the most significant security
threats and implement specialised protocols. A neighbourhood-watch inspired
distributed security scheme for WSNs has been introduced by Langend\"orfer. Its
goal is to increase the variety of attacks a WSN can fend off. A framework of
such complexity has to be designed in multiple steps. Here, we introduce an
approach to determine distributions of security means on large-scale static
homogeneous WSNs. Therefore, we model WSNs as undirected graphs in which two
nodes connected iff they are in transmission range. The framework aims to
partition the graph into distinct security means resulting in the targeted
distribution. The underlying problems turn out to be NP hard and we attempt to
solve them using linear programs (LPs). To evaluate the computability of the
LPs, we generate large numbers of random {\lambda}-precision unit disk graphs
(UDGs) as representation of WSNs. For this purpose, we introduce a novel
{\lambda}-precision UDG generator to model WSNs with a minimal distance in
between nodes
Coverage and Connectivity Aware Neural Network Based Energy Efficient Routing in Wireless Sensor Networks
There are many challenges when designing and deploying wireless sensor
networks (WSNs). One of the key challenges is how to make full use of the
limited energy to prolong the lifetime of the network, because energy is a
valuable resource in WSNs. The status of energy consumption should be
continuously monitored after network deployment. In this paper, we propose
coverage and connectivity aware neural network based energy efficient routing
in WSN with the objective of maximizing the network lifetime. In the proposed
scheme, the problem is formulated as linear programming (LP) with coverage and
connectivity aware constraints. Cluster head selection is proposed using
adaptive learning in neural networks followed by coverage and connectivity
aware routing with data transmission. The proposed scheme is compared with
existing schemes with respect to the parameters such as number of alive nodes,
packet delivery fraction, and node residual energy. The simulation results show
that the proposed scheme can be used in wide area of applications in WSNs.Comment: 16 Pages, JGraph-Hoc Journa
Restricted Strip Covering and the Sensor Cover Problem
Given a set of objects with durations (jobs) that cover a base region, can we
schedule the jobs to maximize the duration the original region remains covered?
We call this problem the sensor cover problem. This problem arises in the
context of covering a region with sensors. For example, suppose you wish to
monitor activity along a fence by sensors placed at various fixed locations.
Each sensor has a range and limited battery life. The problem is to schedule
when to turn on the sensors so that the fence is fully monitored for as long as
possible. This one dimensional problem involves intervals on the real line.
Associating a duration to each yields a set of rectangles in space and time,
each specified by a pair of fixed horizontal endpoints and a height. The
objective is to assign a position to each rectangle to maximize the height at
which the spanning interval is fully covered. We call this one dimensional
problem restricted strip covering. If we replace the covering constraint by a
packing constraint, the problem is identical to dynamic storage allocation, a
scheduling problem that is a restricted case of the strip packing problem. We
show that the restricted strip covering problem is NP-hard and present an O(log
log n)-approximation algorithm. We present better approximations or exact
algorithms for some special cases. For the uniform-duration case of restricted
strip covering we give a polynomial-time, exact algorithm but prove that the
uniform-duration case for higher-dimensional regions is NP-hard. Finally, we
consider regions that are arbitrary sets, and we present an O(log
n)-approximation algorithm.Comment: 14 pages, 6 figure
A Dependable Slepian-Wolf Coding Based Clustering Algorithm for Data Aggregation in Wireless Sensor Networks
International audienceThis paper considers the Slepian-Wolf coding based data aggregation problem and the corresponding dependable clustering problem in wireless sensor networks (WSNs). A dependable Slepian-Wolf coding based clustering (DSWC) algorithm is proposed to provide dependable clustering against cluster-head failures. The proposed D-SWC algorithm attempts to elect a primary cluster head and a backup cluster head for each cluster member during clustering so that once a failure occurs to the primary cluster head the cluster members within the failed cluster can promptly switchover to the backup cluster head and thus recover the connectivity of the failed cluster to the data sink without waiting for the next-round clustering to be performed. Simulation results show that the DSWC algorithm can effectively increase the amount of data transmitted to the data sink as compared with an existing nondependable clustering algorithm for Slepian-Wolf coding based data aggregation in WSNs
Optimisation problems in wireless sensor networks : Local algorithms and local graphs
This thesis studies optimisation problems related to modern large-scale distributed systems, such as wireless sensor networks and wireless ad-hoc networks. The concrete tasks that we use as motivating examples are the following: (i) maximising the lifetime of a battery-powered wireless sensor network, (ii) maximising the capacity of a wireless communication network, and (iii) minimising the number of sensors in a surveillance application. A sensor node consumes energy both when it is transmitting or forwarding data, and when it is performing measurements. Hence task (i), lifetime maximisation, can be approached from two different perspectives. First, we can seek for optimal data flows that make the most out of the energy resources available in the network; such optimisation problems are examples of so-called max-min linear programs. Second, we can conserve energy by putting redundant sensors into sleep mode; we arrive at the sleep scheduling problem, in which the objective is to find an optimal schedule that determines when each sensor node is asleep and when it is awake. In a wireless network simultaneous radio transmissions may interfere with each other. Task (ii), capacity maximisation, therefore gives rise to another scheduling problem, the activity scheduling problem, in which the objective is to find a minimum-length conflict-free schedule that satisfies the data transmission requirements of all wireless communication links. Task (iii), minimising the number of sensors, is related to the classical graph problem of finding a minimum dominating set. However, if we are not only interested in detecting an intruder but also locating the intruder, it is not sufficient to solve the dominating set problem; formulations such as minimum-size identifying codes and locatingâdominating codes are more appropriate. This thesis presents approximation algorithms for each of these optimisation problems, i.e., for max-min linear programs, sleep scheduling, activity scheduling, identifying codes, and locatingâdominating codes. Two complementary approaches are taken. The main focus is on local algorithms, which are constant-time distributed algorithms. The contributions include local approximation algorithms for max-min linear programs, sleep scheduling, and activity scheduling. In the case of max-min linear programs, tight upper and lower bounds are proved for the best possible approximation ratio that can be achieved by any local algorithm. The second approach is the study of centralised polynomial-time algorithms in local graphs â these are geometric graphs whose structure exhibits spatial locality. Among other contributions, it is shown that while identifying codes and locatingâdominating codes are hard to approximate in general graphs, they admit a polynomial-time approximation scheme in local graphs