8,629 research outputs found
Sleep Deprivation Attack Detection in Wireless Sensor Network
Deployment of sensor network in hostile environment makes it mainly
vulnerable to battery drainage attacks because it is impossible to recharge or
replace the battery power of sensor nodes. Among different types of security
threats, low power sensor nodes are immensely affected by the attacks which
cause random drainage of the energy level of sensors, leading to death of the
nodes. The most dangerous type of attack in this category is sleep deprivation,
where target of the intruder is to maximize the power consumption of sensor
nodes, so that their lifetime is minimized. Most of the existing works on sleep
deprivation attack detection involve a lot of overhead, leading to poor
throughput. The need of the day is to design a model for detecting intrusions
accurately in an energy efficient manner. This paper proposes a hierarchical
framework based on distributed collaborative mechanism for detecting sleep
deprivation torture in wireless sensor network efficiently. Proposed model uses
anomaly detection technique in two steps to reduce the probability of false
intrusion.Comment: 7 pages,4 figures, IJCA Journal February 201
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Progressive Processing of Continuous Range Queries in Hierarchical Wireless Sensor Networks
In this paper, we study the problem of processing continuous range queries in
a hierarchical wireless sensor network. Contrasted with the traditional
approach of building networks in a "flat" structure using sensor devices of the
same capability, the hierarchical approach deploys devices of higher capability
in a higher tier, i.e., a tier closer to the server. While query processing in
flat sensor networks has been widely studied, the study on query processing in
hierarchical sensor networks has been inadequate. In wireless sensor networks,
the main costs that should be considered are the energy for sending data and
the storage for storing queries. There is a trade-off between these two costs.
Based on this, we first propose a progressive processing method that
effectively processes a large number of continuous range queries in
hierarchical sensor networks. The proposed method uses the query merging
technique proposed by Xiang et al. as the basis and additionally considers the
trade-off between the two costs. More specifically, it works toward reducing
the storage cost at lower-tier nodes by merging more queries, and toward
reducing the energy cost at higher-tier nodes by merging fewer queries (thereby
reducing "false alarms"). We then present how to build a hierarchical sensor
network that is optimal with respect to the weighted sum of the two costs. It
allows for a cost-based systematic control of the trade-off based on the
relative importance between the storage and energy in a given network
environment and application. Experimental results show that the proposed method
achieves a near-optimal control between the storage and energy and reduces the
cost by 0.989~84.995 times compared with the cost achieved using the flat
(i.e., non-hierarchical) setup as in the work by Xiang et al.Comment: 41 pages, 20 figure
Distributed Object Tracking Using a Cluster-Based Kalman Filter in Wireless Camera Networks
Local data aggregation is an effective means to save sensor node energy and prolong the lifespan of wireless sensor networks. However, when a sensor network is used to track moving objects, the task of local data aggregation in the network presents a new set of challenges, such as the necessity to estimate, usually in real time, the constantly changing state of the target based on information acquired by the nodes at different time instants. To address these issues, we propose a distributed object tracking system which employs a cluster-based Kalman filter in a network of wireless cameras. When a target is detected, cameras that can observe the same target interact with one another to form a cluster and elect a cluster head. Local measurements of the target acquired by members of the cluster are sent to the cluster head, which then estimates the target position via Kalman filtering and periodically transmits this information to a base station. The underlying clustering protocol allows the current state and uncertainty of the target position to be easily handed off among clusters as the object is being tracked. This allows Kalman filter-based object tracking to be carried out in a distributed manner. An extended Kalman filter is necessary since measurements acquired by the cameras are related to the actual position of the target by nonlinear transformations. In addition, in order to take into consideration the time uncertainty in the measurements acquired by the different cameras, it is necessary to introduce nonlinearity in the system dynamics. Our object tracking protocol requires the transmission of significantly fewer messages than a centralized tracker that naively transmits all of the local measurements to the base station. It is also more accurate than a decentralized tracker that employs linear interpolation for local data aggregation. Besides, the protocol is able to perform real-time estimation because our implementation takes into consideration the sparsit- - y of the matrices involved in the problem. The experimental results show that our distributed object tracking protocol is able to achieve tracking accuracy comparable to the centralized tracking method, while requiring a significantly smaller number of message transmissions in the network
Energy-Efficient Data Acquisition in Wireless Sensor Networks through Spatial Correlation
The application of Wireless Sensor Networks (WSNs) is restrained by their often-limited lifetime. A sensor node's lifetime is fundamentally linked to the volume of data that it senses, processes and reports. Spatial correlation between sensor nodes is an inherent phenomenon to WSNs, induced by redundant nodes which report duplicated information. In this paper, we report on the design of a distributed sampling scheme referred to as the 'Virtual Sampling Scheme' (VSS). This scheme is formed from two components: an algorithm for forming virtual clusters, and a distributed sampling method. VSS primarily utilizes redundancy of sensor nodes to get only a subset to sense the environment at any one time. Sensor nodes that are not sensing the environment are in a low-power sleep state, thus conserving energy. Furthermore, VSS balances the energy consumption amongst nodes by using a round robin method
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