Some sensor network applications involve an aerial deployment of many sensor nodes over a particular area of interest. In this context, current range-free localization proposals, based on an iterative refinement and e...
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Some sensor network applications involve an aerial deployment of many sensor nodes over a particular area of interest. In this context, current range-free localization proposals, based on an iterative refinement and exchange of node estimations, are not directly applicable, because they introduce a high traffic overhead. In this paper, we propose to control this overhead by means of avoiding the transmission of certain localization packets. the criterion applied by this new localization technique to filter packets is based on the amount of improvement shown after updating an estimation and the time from the last transmission. We also tune this filter in order to find an optimal trade-off between the benefit in traffic and the penalty in time.
distributed computation of Voronoi cells in sensor networks, i.e. computingthe locus of points in a sensor field closest to a given sensor, is a key building block that supports a number of applications in boththe d...
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distributed computation of Voronoi cells in sensor networks, i.e. computingthe locus of points in a sensor field closest to a given sensor, is a key building block that supports a number of applications in boththe data and control planes. For example, knowledge of Voronoi cells facilitates efficient methods for computingthe piece-wise approximation of a field, whereby each sensor acts as a representative for the set of points in its Voronoi cell; awareness of Voronoi boundaries and Voronoi neighbors is also useful in load balancing and energy conservation. the methods currently advocated for distributed Voronoi computation in sensor networks are heuristic approximations that can introduce significant inaccuracies that are difficult to rigorously quantify; we demonstrate that these methods may err by a factor of 5 or more in some circumstances. We present and prove an exact method which eliminates these inaccuracies, at the cost of increased messaging overhead, but without necessitating contact withthe entire network. To our knowledge, this is the first distributed algorithm that computes accurate Voronoi cells without requiring all-to-all communication. We implement it as a TinyOS module and quantitatively analyze its performance.
A proxy signature scheme allows an entity to delegate his/her signing capability to another entity in such a way that the latter can sign messages on behalf of the former. Such schemes have been suggested for use in a...
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When extremely low-energy processing is required, the choice of data representation makes a tremendous difference. Each representation (e.g. frequency domain, residue coded, log-scale) comes with a unique set of trade...
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ISBN:
(纸本)9781450362405
When extremely low-energy processing is required, the choice of data representation makes a tremendous difference. Each representation (e.g. frequency domain, residue coded, log-scale) comes with a unique set of trade-offs - some operations are easier in that domain while others are harder. We demonstrate that race logic, in which temporally coded signals are getting processed in a dataflow fashion, provides interesting new capabilities for in-sensor processing applications. Specifically, with an extended set of race logic operations, we show that tree-based classifiers can be naturally encoded, and that common classification tasks can be implemented efficiently as a programmable accelerator in this class of logic. To verify this hypothesis, we design several race logic implementations of ensemble learners, compare them against state-of-the-art classifiers, and conduct an architectural design space exploration. Our proof-of-concept architecture, consisting of 1,000 reconfigurable Race Trees of depth6, will process 15.2M frames/s, dissipating 613mW in 14nm CMOS.
Blockchain as an emergent technology is quickly gaining ground as a decentralised distributed trustless database in the form of a linked-list data structure. this paper discusses the non-deterministic nature of public...
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ISBN:
(纸本)9781728147635
Blockchain as an emergent technology is quickly gaining ground as a decentralised distributed trustless database in the form of a linked-list data structure. this paper discusses the non-deterministic nature of public blockchain consensus protocols and argues that the probabilistic validity of a new block does not provide adequate guarantees of accuracy and integrity which in the case of cryptocurrencies could result in quite substantial asset losses. the contribution this paper adds is to spark a new line of thinking in regards to the validity of data in blockchains.
In this paper, we investigate a scenario where a distributed satellite system (DSS) serves as a distributedcomputing platform within the space-terrestrial integrated network (STIN), catering to ground users. To enhan...
In this paper, we investigate a scenario where a distributed satellite system (DSS) serves as a distributedcomputing platform within the space-terrestrial integrated network (STIN), catering to ground users. To enhance the scalability of the distributedcomputing system and adapt to variations in user request traffic, we propose an auto-scaling service strategy based on traffic prediction. the prediction is implemented using the long short-term memory recurrent neural network (LSTM-RNN) method. Numerical results are presented to demonstrate the superiority of our proposed traffic prediction algorithm compared to existing approaches, while experimental findings confirm that our auto-scaling service strategy effectively enhances system performance in terms of service quality.
Coverage estimation is one of the fundamental issues in many applications of sensor networks. Coverage estimation in visual sensor networks (VSNs) is more challenging than in conventional 1-D scalar sensor networks (S...
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Coverage estimation is one of the fundamental issues in many applications of sensor networks. Coverage estimation in visual sensor networks (VSNs) is more challenging than in conventional 1-D scalar sensor networks (SSNs) due to the directional sensing characteristic of cameras and the existence of visual occlusions in crowded environments. Moreover, deployment of heterogeneous visual sensors and existence of heterogeneous targets in the sensing field makes the coverage estimation problem even more challenging. In this paper, we study the coverage estimation problem in heterogeneous VSNs. We first investigate into a new target detection model, referred to as the "certainty-based target detection" as compared to the traditional "occupancy-based target detection" to facilitate the formulation of the visual coverage estimation. By adopting the new target detection model, we then derive the closed-form solution for the visual coverage estimation problem in heterogeneous VSNs. Our formulation also allows us to take boththe presence of visual occlusions and boundary effect into consideration. Results from simulation validate the theoretical formulation, especially when the boundary effect is considered.
We propose a novel input interface for touch panel devices that employs the built-in camera of the devices to measure multi-axis force information applied by a user. the user attaches the proposed interface, which con...
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ISBN:
(纸本)9781467327428
We propose a novel input interface for touch panel devices that employs the built-in camera of the devices to measure multi-axis force information applied by a user. the user attaches the proposed interface, which consists of an elastic body and markers, to the built-in camera. the touch panel device can then detect force information applied by the user to the elastic body based on vision-based force sensor technology called GelForce. We implemented a prototype and confirmed that the proposed interface can measure 3-axis force. We then conducted user studies to verify that the proposed interface is acceptable to users of touch panel devices.
Applications for wireless sensor networks may be decomposed into the deployment of tasks on different sensor nodes in the network. Task allocation algorithms assign these tasks to specific sensor nodes in the network ...
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Applications for wireless sensor networks may be decomposed into the deployment of tasks on different sensor nodes in the network. Task allocation algorithms assign these tasks to specific sensor nodes in the network for execution. Given the resource-constrained and distributed nature of wireless sensor networks (WSNs), existing static (offline) task scheduling may not be practical. therefore there is a need for an adaptive task allocation scheme that accounts for the characteristics of the WSN environment such as unexpected communication delay and node failure. In this paper, we focus on task allocation in WSNs which is performed withthe aim of achieving a fair energy balance amongst the sensor nodes while minimizing delay using a market-based architecture. In this architecture, nodes are modeled as sellers communicating a deployment price for a task to the consumer. To address this task allocation problem, proposed price formulation is used as it continuously adapts to changes of the availabilities of resources. this scheme also accommodates for the node failure during task assignment. the centralized and distributed message exchanged mechanisms between the nodes (sellers) and task allocator (consumer) are proposed to determine the winner among the sellers withthe goal of reducing overhead and energy consumption. Simulation results show that, compared with a static scheduling scheme with an objective in energy balancing, the proposed scheme adapts to new environmental changes and uncertain network condition more dynamically and achieves a much better performance on energy balancing.
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