T.C. Subbulakshmi

Bio: T.C. Subbulakshmi is an academic researcher. The author has contributed to research in topics: Euclidean distance & Network packet. The author has an hindex of 1, co-authored 1 publications receiving 6 citations.

Multihop distance estimation with Greedy approach in wireless sensor networks

Abstract: A wireless sensor network consists of spatially distributed autonomous sensors to cooperatively monitor physical or environmental conditions. In addition to one or more sensors, each node in a sensor network is typically equipped with a radio transceiver or other wireless communications device, a small microcontroller, and an energy source, usually a battery. A sensor network normally constitutes a wireless ad-hoc network, meaning that each sensor supports a multi-hop routing algorithm several nodes may forward data packets to the base station. Location and internodes distance estimation is of profound importance for various WSN applications. Similarly, estimation of the hop distance between two network locations is equivalent to estimating the minimum number of hops, which leads to maximization of the distance covered in multihop paths. Furthermore, hop distance estimation is closely related with transmission delay estimation and minimization of multihop energy consumption. Determination of the maximum multihop Euclidean distance corresponding to a given hop distance in a 2D network is a complex problem. The accuracy of the Gaussian pdf model depends on the number of hops and the chosen parameters, which affect the obtained error ranges. A Greedy distance maximization model is proposed, which approximates the maximum multihop Euclidean distance and evaluates the distribution of the obtained multihop distance in planar networks.

Event reliability in Wireless Sensor Networks

Abstract: Ensuring reliable transport of data in resource constrained Wireless Sensor Networks (WSNs) is one of the primary concerns. The two reliability mechanisms typically used in WSNs are packet reliability and event reliability. Packet reliability requires all packets from all the sensor nodes to reach the sink that can result in wastage of sensors' limited energy resources. The sensing regions of densely deployed sensor nodes often overlap with one another and data from nodes that are in close proximity tend to exhibit high level of spatial locality. This introduces the concept of event reliability where a reliable transfer of event data from each sensing region in a sensor network is sufficient. This paper proposes the Event Reliability Protocol (ERP) that enables reliable transfer of packets containing information about an event to the sink while minimizing similar redundant packets from nodes in the vicinity of one another. ERP builds on the spatial locality condition and employs an implicit acknowledgement (iACK) mechanism with region-based selective retransmissions. The performance of ERP is evaluated and compared against a commonly used iACK scheme to show that ERP significantly improves event information delivery and network scalability, thus maintaining good coverage of events in the sensor network.

Position estimation method, system, and position estimation apparatus

Abstract: A position estimation apparatus acquires, for base devices, the number of hops from a target sensor node to a base device. The position estimation apparatus calculates, for each base device, a distribution of estimated distances corresponding to the total hops, based on the number of hops and a distribution of estimated distances stored in a storage device. The position estimation apparatus calculates, for each base device, a distribution of estimated positions within a given region, based on the distribution of estimated distances, information concerning a range of the given region, and information concerning a position of the base device. The position estimation apparatus calculates an index that indicates a probability of a position of the target sensor node within the given region, based on the sum of distributions of estimated positions concerning the base stations.

Position estimation method, system, and position estimation device

Abstract: A position estimation apparatus (100) acquires, for base devices (102), the number of hops from a target sensor node (101) to a base device (102). The position estimation apparatus (100) calculates, for each base device (102), a distribution of estimated distances corresponding to the total hops, based on the number of hops and a distribution of estimated distances stored in a storage device (110). The position estimation apparatus (100) calculates, for each base device (102), a distribution of estimated positions within a given region (A), based on the distribution of estimated distances, information concerning a range of the given region (A), and information concerning a position of the base device (102). The position estimation apparatus (100) calculates an index that indicates a probability of a position of the target sensor node (101) within the given region (A), based on the sum of distributions of estimated positions concerning the base stations.

Position estimation device

Abstract: PROBLEM TO BE SOLVED: To reduce an error in estimating a target position in a wireless mesh network.SOLUTION: A position estimation device according to a first embodiment estimates the position of a second terminal in a wireless network provided with a plurality of terminals including three or more first terminals whose positions are known and a second terminal whose position is unknown. The position estimation device calculates first connection probability with which two out of the plurality of terminals are connected, on the basis of the distance and the minimum number of hops between the two terminals. The position estimation device finds likelihood on the basis of the first connection probability, and estimates the position of the second terminal using the likelihood.SELECTED DRAWING: Figure 6

Position estimation method, system, and position estimation device

Abstract: A position estimation device (100), with regard to each parent apparatus (102), acquires a number of hops, from object sensor nodes (101) to a parent apparatus (102), which is based on a communication result. The position estimation device (100), for each of the parent apparatuses (102), on the basis of the acquired number of hops and a distribution of estimated distances within a storage device (110), calculates a distribution of the estimated distances for which communication with the parent apparatus (102) corresponds to the acquired number of hops. The position estimation device (100), for each of the parent apparatuses (102), calculates a distribution of the estimated positions within a predetermined area (A) on the basis of the distribution of the estimated distances, information representing the range of the predetermined area (A), and positional information of the parent apparatuses (102). The position estimation device (100), on the basis of the sum of distributions of the estimated positions for each of the parent apparatuses (102), for each of the positions within the predetermined area (A), calculates an index indicating the likelihood of being in a position of an object sensor node (101) and derives an estimated position of the object sensor node (101).