In data aggregation, sensor measurements from the whole sensory field or a sub-field are collected as a single report at an actor by using aggregate functions such as sum, average, maximum, minimum, count, deviation, and so on. We propose a localized delay-bounded and energy-efficient data aggregation (DEDA) protocol for request-driven wireless sensor networks with IEEE 802.11 carrier sense multiple access with collision avoidance run at media access control layer. This protocol uses a novel two-stage delay model, which measures end-to-end delay by using either hop count or degree sum along a routing path depending on traffic intensity. It models the network as a unit disk graph (UDG) and constructs a localized minimal spanning tree (LMST) sub-graph. Using only edges from LMST, it builds a shortest-path (thus energyefficient) tree rooted at the actor for data aggregation. The tree is used without modification if it generates acceptable delay, compared with a given delay bound. Otherwise, it is adjusted by replacing LMST sub-paths with UDG edges. The adjustment is done locally on the fly, according to the desired progress value computed at each node. We further propose to integrate DEDA with a localized sensor activity scheduling algorithm and a localized connected dominating set algorithm, yielding two DEDA variants, to improve its energy efficiency and delay reliability. Through an extensive set of simulation, we evaluate the performance of DEDA with various network parameters. Our simulation results indicate that DEDA far outperforms the only existing competing protocol. available from neighbors for making protocol decisions. Although this type of protocols yield sub-optimal results, they are efficient and scalable for large-scale networks and are remarkable in practice. MotivationSensors are normally powered by low-energy batteries. Manual replacement or recharge of sensor batteries is infeasible most of time because of operational factors such as human inaccessibility to the sensory field or tight maintenance budget. It is therefore highly desirable to prolong the lifetime of a WSN as a whole by minimizing and balancing energy usage among individual sensors. In critical real-time scenarios such as disaster management, emergency rescue, battle field surveillance, and so on, sensor reports are often required to arrive at actors with bounded delay so as to ensure timely event response. Failure to do so may cause loss of lives and damage to economy.Existing data aggregation protocols usually require centralized control and/or emphasize on energy efficiency. They seldom consider the delay problem. The only known localized delay-bounded power-aware data aggregation protocol [4], referred to as MS here, has major drawbacks in energy saving as well as in delay modeling, and it thus has limited effect on prolonging network lifetime and meeting delay requirement. Motivated by the insufficiency and incompleteness of previous work, in this article we address how to achieve power optimality in data aggregation while respe...
With the rise of location-based services and the rapidly growing requirements related to their applications, indoor localization based on channel state information–multiple-input multiple-output (CSI-MIMO) has become an important research topic. However, indoor localization based on CSI-MIMO has some disadvantages, including noise and high data dimensions. To overcome the above drawbacks, we proposed a novel method of indoor localization based on CSI-MIMO, named SICD. For SICD, a novel localization fingerprint was first designed which can reflect the time–frequency and space–frequency characteristics of CSI-MIMO under a single access point (AP). To reduce the redundancy in the data of CSI-MIMO amplitude, we developed a data dimensionality reduction algorithm. Moreover, by leveraging a log-normal distribution, we calculated the conditional probability of the naive Bayes classifier, which was used to predict the moving object’s location. Compared with other state-of-the-art methods, the results of the experiment confirm that the SICD effectively improves localization accuracy.
With the increasing demand of location-based services, neural network (NN)-based intelligent indoor localization has attracted great interest due to its high localization accuracy. However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indoor localization system, including denoising NN and residual network (ResNet), to predict the location of moving object by the channel state information (CSI). In the ResNet, to prevent overfitting, we replace all the residual blocks by the stochastic residual blocks. Specially, we explore the long-range stochastic shortcut connection (LRSSC) to solve the degradation problem and gradient vanishing. To obtain a large receptive field without losing information, we leverage the dilated convolution at the rear of the ResNet. Experimental results are presented to confirm that our system outperforms state-of-the-art methods in a representative indoor environment.
The intelligent indoor localization based on WIFI is increasingly concerned for its universality. However, in practical applications, its indoor localization accuracy is limited by noises, diffractions and multipath effects. To overcome these drawbacks, we design a new intelligence indoor localization system based on Channel State Information (CSI) of the wireless signal from Multiple Input Multiple Output (MIMO), named IILC. In IILC, the initial amplitude information is first processed in the measured CSI data, which can effectively suppress the impact from noise and other interference. Next, we explore a method to construct radio image. It can make full use of space-frequency information and time-frequency information from CSI-MIMO to obtain more location information. Then, we design a new deep learning network to obtain the optimal effective of radio image classification. Moreover, a mixed-norm is proposed to impose sparsity penalty and overfit constraint on the loss function, which makes the valuable feature units active and the others inactive. The experimental results verify that IILC system has excellent performance. The overall localization accuracy of IILC in the office scene can reach 97.10%, and the probability of localization error within 1.2m is 86.21%.
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