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For Wireless Sensor Networks, energy efficiency is always a key consideration in system design. Compressed sensing is a new theory which has promising prospects in WSNs. However, how to construct a sparse projection matrix is a problem. In this paper, based on a Bayesian compressed sensing framework, a new adaptive algorithm which can integrate rou...
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... is shown in Figure 3, there are in total 18 transmissions, but only nine nodes' information is collected. To improve this, the structure shown in Figure 4 is another option. In this style, all selected nodes are arranged in a ring structure. ...
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... multiple copies of the packet are reunited when necessary to save energy. This structure takes the advantage of the structures proposed in Figures 4 and 5, and has the virtue of both energy efficiency and short collection path. In this paper, we propose to design a multi-ring routing structure as shown in Figure 6. ...
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... For instance, Decision Trees, Neural Networks, Random Forest, Support Vector Machine (SVM), and k-nearest neighbor (K-NN) belong to supervised learning [5]. The supervised learning algorithms have been efficiently applied to solve routing problems [29][30][31][32][33][34][35][36], localization problems [44][45][46][47][48][49][50][51][52][53], event detection problems [56], target tracking [55], and sensor fusion issues in WSNs [37][38][39][40][41][42][43]. ...
... If a set of inputs are represented by Y 1 , Y 2 , Y 3 … Y n, and returns, a label θ, the probability of p(θ) must be maximized. Several issues in WSNs such as routing [34][35][36], data localization [49][50][51], aggregation [41,42], fault detection, connectivity, and coverage problems [65] have been solved by Bayesian learning methods. ...
... Few Naïve Bayes routing protocols for WSNs are discussed in [34][35][36]. In [34], the research work focuses on the selection of CHs for routing and prolongs the network lifetime by reducing energy consumption. ...
Energy conservation is the primary task in Wireless Sensor Networks (WSNs) as these tiny sensor nodes are the backbone of today’s Internet of Things (IoT) applications. These nodes rely exclusively on battery power to maneuver in hazardous environments. So, there is a requirement to study and design efficient, robust communication protocols to handle the challenges of the WSNs to make the network operational for a long time. Although traditional technologies solve many issues in WSNs, it may not derive an accurate mathematical model for predicting system behavior. So, some challenging tasks like routing, data fusion, localization, and object tracking are handled by low complexity mathematical models to define system behavior. In this paper, an effort has been made to provide a big outlook to the current “researchers” on machine learning techniques that have been employed to handle various issues in WSNs, and special attention has been given to routing problems.
... A major responsibilities of supervised learning algorithms are to generate the model which represents relationships and dependency links between input features and forecast objective outputs. Supervised learning solve various challenges in WSNs such as localization [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] , coverage problems [26][27][28][29][30][31] , anomaly and fault detection [32][33][34][35][36][37][38][39][40][41][42][43][44][45] , routing [46][47][48][49][50][51][52][53] , MAC [54] , data aggregation [55][56][57][58][59][60][61][62][63][64][65][66][67] , synchronization [68][69][70][71] , congestion control [72][73][74] , target tracking [75][76][77][78] , event detection [79][80][81] , and energy harvesting [82,83] . Supervised learning categorized into regression and classification. ...
... Recently, several WSNs problems are solved based on the Bayesian learning strategies to improve the efficiency of the network. The issues are localization [21][22][23][24][25] , coverage [31] , anomaly & fault detection [37,38,[41][42][43] , routing [51][52][53] , data aggregation [63][64][65][66][67] , synchronization [71] , target tracking [75][76][77][78]103] , event detection [104] , and mobile sink path selection [105] . ...
... Naïve Bayes guarantees that even new features added or changed in the network dynamically. A new adaptive integrated routing framework has been presented in [52] for data collection based on a Bayesian technique. In the projected technique, an adaptive projection vector is constructed in each iteration of routing by introducing a new target node selection. ...
Wireless sensor network (WSN) is one of the most promising technologies for some real-time applications because of its size, cost-effective and easily deployable nature. Due to some external or internal factors, WSN may change dynamically and therefore it requires depreciating dispensable redesign of the network. The traditional WSN approaches have been explicitly programmed which make the networks hard to respond dynamically. To overcome such scenarios, machine learning (ML) techniques can be applied to react accordingly. ML is the process of self-learning from the experiences and acts without human intervention or re-program. The survey of the ML techniques for WSNs is presented in [1], covering period of 2002 – 2013. In this survey, we present various ML-based algorithms for WSNs with their advantages, drawbacks, and parameters effecting the network lifetime, covering the period from 2014–March 2018. In addition, we also discuss ML algorithms for synchronization, congestion control, mobile sink scheduling and energy harvesting. Finally, we present a statistical analysis of the survey, the reasons for selection of a particular ML techniques to address a issue in WSNs followed by some discussion on the open issues.
... As mentioned in the previous section, data reconstruction accuracy is a concern while using CS. To date, numerous studies exist which are aimed at improving the accuracy of the reconstructed data while using CS [22][23][24][25]. These have mainly investigated the routing issue and its effects on the reconstruction accuracy. ...
The high number of transmissions in sensor nodes having a limited amount of energy leads to a drastic decrease in the lifetime of wireless sensor networks. For dense sensor networks, the provided data potentially have spatial and temporal correlations. The correlations between the data of the nodes make it possible to utilize compressive sensing theory during the data gathering phase; however, applying this technique leads to some errors during the reconstruction phase. In this paper, a method based on weighted spatial-temporal compressive sensing is proposed to improve the accuracy of the reconstructed data. Simulation results confirm that the reconstruction error of the proposed method is approximately 16 times less than the closest compared method. It should be noted that due to applying weighted spatial-temporal compressive sensing, some extra transmissions are posed to the network. However, considering both lifetime and accuracy factors as a compound metric, the proposed method yields a 12% improvement compared to the closest method in the literature.
... Charalampidis et al. introduced an adaptive-rate CS framework for energy efficient IoT applications, which is able to adjust the compression rate according to the time-varying nature of the data acquired in the IoT [12]. Liu et al. introduced a new adaptive data collection method based on a Bayesian CS framework [13]. In [14], Liu et al. introduced the concept of Multiple Measurement Vector CS for spread- spectrum-based IoT applications. ...
... In order to reduce the amount of sensing data, we need to compress the data in the network. Compression-based data collection techniques given by various researchers [24][25][26][27] make it possible to directly acquire just the important information about the sensing field by not acquiring that part of the data that would eventually just be "thrown away" by lossy compression. So, compression techniques provide support for energy-efficient data collection. ...
To increase a wireless sensor network's lifetime, energy-efficient strategies are proposed by researchers from time to time. Most of these are developed under the assumption that the energy requirement during data collection is much less than that during com munication. However, for many practical application scenarios, this assumption does not hold, especially when specific sensors are used for monitoring complex phenomena. The network lifetime of WSNs can be substantially improved with energy-efficient data collection techniques, which has the potential to make these networks more com pliant for future wireless applications seeking huge energy consumption. This chapter deals with energy-efficient data collection techniques for wireless sensor networks
... It has been applied to various fields, including optical and radar remote sensing [4][5][6][7][8][9][10][11]. CS works on the assumption of the sparsity of the scene being sensed, relies on the informational transferability of the sensing/measurement matrices in capturing the information content in the underlying signal (or scene in the context of radar) and operates through algorithms that can reconstruct the sparse signal from under-sampled data [7,[12][13][14][15][16]. ...
Compressive sampling or compressed sensing (CS) works on the assumption of the sparsity or compressibility of the underlying signal, relies on the trans-informational capability of the measurement matrix employed and the resultant measurements, operates with optimization-based algorithms for signal reconstruction and is thus able to complete data compression, while acquiring data, leading to sub-Nyquist sampling strategies that promote efficiency in data acquisition, while ensuring certain accuracy criteria. Information theory provides a framework complementary to classic CS theory for analyzing information mechanisms and for determining the necessary number of measurements in a CS environment, such as CS-radar, a radar sensor conceptualized or designed with CS principles and techniques. Despite increasing awareness of information-theoretic perspectives on CS-radar, reported research has been rare. This paper seeks to bridge the gap in the interdisciplinary area of CS, radar and information theory by analyzing information flows in CS-radar from sparse scenes to measurements and determining sub-Nyquist sampling rates necessary for scene reconstruction within certain distortion thresholds, given differing scene sparsity and average per-sample signal-to-noise ratios (SNRs). Simulated studies were performed to complement and validate the information-theoretic analysis. The combined strategy proposed in this paper is valuable for information-theoretic orientated CS-radar system analysis and performance evaluation.
Wireless sensor network (WSN) consists of sparsely distributed, low energy, and bandwidth sensor nodes that collect sensed data. In WSNs, these data are initially converted from analog to digital signals and transmitted to base stations. Routing in WSNs is the process of determining the most efficient path for data transmission among various sensor nodes. In routing, small sensor nodes use limited network bandwidth and energy to capture and transmit a limited amount of data. However, with the advancement of big data and IoT, large-scale sensors are used to route massive amounts of data. Routing with this huge data consumes a lot of network bandwidth and energy and thus reduces the lifespan of the network. Thus, for energy-efficient routing (EER), there is a need for data optimization that can be achieved by many machine learning (ML) algorithms. Many researchers have devised various noteworthy works related to ML to have an EER in WSNs. This chapter reviews the existing ML-based routing algorithms in WSNs.
