11,966 research outputs found
K-coverage in regular deterministic sensor deployments
An area is k-covered if every point of the area is covered by at least k sensors. K-coverage is necessary for many applications, such as intrusion detection, data gathering, and object tracking. It is also desirable in situations where a stronger environmental monitoring capability is desired, such as military applications. In this paper, we study the problem of k-coverage in deterministic homogeneous deployments of sensors. We examine the three regular sensor deployments - triangular, square and hexagonal deployments - for k-coverage of the deployment area, for k ≥ 1. We compare the three regular deployments in terms of sensor density. For each deployment, we compute an upper bound and a lower bound on the optimal distance of sensors from each other that ensure k-coverage of the area. We present the results for each k from 1 to 20 and show that the required number of sensors to k-cover the area using uniform random deployment is approximately 3-10 times higher than regular deployments
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
A Survey on Wireless Sensor Network Security
Wireless sensor networks (WSNs) have recently attracted a lot of interest in
the research community due their wide range of applications. Due to distributed
nature of these networks and their deployment in remote areas, these networks
are vulnerable to numerous security threats that can adversely affect their
proper functioning. This problem is more critical if the network is deployed
for some mission-critical applications such as in a tactical battlefield.
Random failure of nodes is also very likely in real-life deployment scenarios.
Due to resource constraints in the sensor nodes, traditional security
mechanisms with large overhead of computation and communication are infeasible
in WSNs. Security in sensor networks is, therefore, a particularly challenging
task. This paper discusses the current state of the art in security mechanisms
for WSNs. Various types of attacks are discussed and their countermeasures
presented. A brief discussion on the future direction of research in WSN
security is also included.Comment: 24 pages, 4 figures, 2 table
SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach
This paper presents the development of a Supervisory Control and Data
Acquisition (SCADA) system testbed used for cybersecurity research. The testbed
consists of a water storage tank's control system, which is a stage in the
process of water treatment and distribution. Sophisticated cyber-attacks were
conducted against the testbed. During the attacks, the network traffic was
captured, and features were extracted from the traffic to build a dataset for
training and testing different machine learning algorithms. Five traditional
machine learning algorithms were trained to detect the attacks: Random Forest,
Decision Tree, Logistic Regression, Naive Bayes and KNN. Then, the trained
machine learning models were built and deployed in the network, where new tests
were made using online network traffic. The performance obtained during the
training and testing of the machine learning models was compared to the
performance obtained during the online deployment of these models in the
network. The results show the efficiency of the machine learning models in
detecting the attacks in real time. The testbed provides a good understanding
of the effects and consequences of attacks on real SCADA environmentsComment: E-Preprin
Intrusion-aware Alert Validation Algorithm for Cooperative Distributed Intrusion Detection Schemes of Wireless Sensor Networks
Existing anomaly and intrusion detection schemes of wireless sensor networks
have mainly focused on the detection of intrusions. Once the intrusion is
detected, an alerts or claims will be generated. However, any unidentified
malicious nodes in the network could send faulty anomaly and intrusion claims
about the legitimate nodes to the other nodes. Verifying the validity of such
claims is a critical and challenging issue that is not considered in the
existing cooperative-based distributed anomaly and intrusion detection schemes
of wireless sensor networks. In this paper, we propose a validation algorithm
that addresses this problem. This algorithm utilizes the concept of
intrusion-aware reliability that helps to provide adequate reliability at a
modest communication cost. In this paper, we also provide a security resiliency
analysis of the proposed intrusion-aware alert validation algorithm.Comment: 19 pages, 7 figure
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