21,345 research outputs found
A New Mechanism for Tracking a Mobile Target Using Grid Sensor Networks
Tracking moving targets is one of the important problems of wireless sensor networks. We have considered a sensor network where numerous sensor nodes are spread in a grid like manner. These sensor nodes are capable of storing data and thus act as a separate datasets. The entire network of these sensors act as a set of distributed datasets. Each of these datasets has its local temporal dataset along with spatial data and the geographical coordinates of a given object or target. In this paper an algorithm is introduced that mines global temporal patterns from these datasets and results in the discovery of linear or nonlinear trajectories of moving objects under supervision. The main objective here is to perform in-network aggregation between the data contained in the various datasets to discover global spatio-temporal patterns; the main constraint is that there should be minimal communication among the participating nodes. We present the algorithm and analyze it in terms of the communication costs
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
Dead Reckoning Localization Technique for Mobile Wireless Sensor Networks
Localization in wireless sensor networks not only provides a node with its
geographical location but also a basic requirement for other applications such
as geographical routing. Although a rich literature is available for
localization in static WSN, not enough work is done for mobile WSNs, owing to
the complexity due to node mobility. Most of the existing techniques for
localization in mobile WSNs uses Monte-Carlo localization, which is not only
time-consuming but also memory intensive. They, consider either the unknown
nodes or anchor nodes to be static. In this paper, we propose a technique
called Dead Reckoning Localization for mobile WSNs. In the proposed technique
all nodes (unknown nodes as well as anchor nodes) are mobile. Localization in
DRLMSN is done at discrete time intervals called checkpoints. Unknown nodes are
localized for the first time using three anchor nodes. For their subsequent
localizations, only two anchor nodes are used. The proposed technique estimates
two possible locations of a node Using Bezouts theorem. A dead reckoning
approach is used to select one of the two estimated locations. We have
evaluated DRLMSN through simulation using Castalia simulator, and is compared
with a similar technique called RSS-MCL proposed by Wang and Zhu .Comment: Journal Paper, IET Wireless Sensor Systems, 201
RFID Localisation For Internet Of Things Smart Homes: A Survey
The Internet of Things (IoT) enables numerous business opportunities in
fields as diverse as e-health, smart cities, smart homes, among many others.
The IoT incorporates multiple long-range, short-range, and personal area
wireless networks and technologies into the designs of IoT applications.
Localisation in indoor positioning systems plays an important role in the IoT.
Location Based IoT applications range from tracking objects and people in
real-time, assets management, agriculture, assisted monitoring technologies for
healthcare, and smart homes, to name a few. Radio Frequency based systems for
indoor positioning such as Radio Frequency Identification (RFID) is a key
enabler technology for the IoT due to its costeffective, high readability
rates, automatic identification and, importantly, its energy efficiency
characteristic. This paper reviews the state-of-the-art RFID technologies in
IoT Smart Homes applications. It presents several comparable studies of RFID
based projects in smart homes and discusses the applications, techniques,
algorithms, and challenges of adopting RFID technologies in IoT smart home
systems.Comment: 18 pages, 2 figures, 3 table
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