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In this paper, we propose a compressive sensing (CS)-based channel estimation technique for asymmetrically clipped optical-orthogonal frequency division multiplexing (ACO-OFDM) visible light communications (VLC) in 5G systems. We proposed a modified version of sparsity adaptive matching pursuit (SAMP) algorithm which is named as self-aware step size sparsity adaptive matching pursuit (SS-SAMP) algorithm. It utilizes the built-in features of SAMP and with additional ability to select step size according to the present situation, hence term self-aware, can provide better accuracy and low computational cost. It also does not require any prior knowledge of the sparsity of the signal which makes it self-sufficient. CS-based algorithms such as orthogonal matching pursuit (OMP), SAMP, and our proposed SS-SAMP were implemented on ACO-OFDM VLC. The paper is supported by simulation results which demonstrate performance of proposed scheme in terms of bit error rate (BER), mean square error (MSE), computational complexity, and key VLC parameter (LED nonlinearity, shot noise, thermal noise, channel response, and peak-to-average power ratio (PAPR). It is shown that the SS-SAMP is a good candidate for ACO-OFDM-based VLC that are mobile and have limited processing power, based on its performance and computational complexity. [ABSTRACT FROM AUTHOR]

Cyclostationarity detectors have been studied in the past few years as an efficient means for signal detection under low-SNR conditions. On the other hand, some knowledge about the signal is needed at the detector. This is typically the case in Cognitive Radio spectrum secondary usage, where the primary system is known. This paper focuses on two hardware architectures of cyclostationarity detectors for OFDM signals. The first architecture aims at secondary ISM band use, considering IEEE802.11a/g as the primary system. In this scenario, low latency is required. The second architecture targets TV band secondary usage, where DVB-T signals must be detected at very low SNR. The paper focuses on the architectural tradeoffs that the designer has to face, and how his/her choices will influence either performance or complexity. Hardware complexity evaluation on FPGA is provided for detectors that have been tested in the laboratory under real conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a new geolocation technique to improve the accuracy of the position estimate of a single unknown (anonymous) radio wave emitter. We consider a factor graph (FG)-based geolocation technique, where the input are the samples of direction-of-arrival (DOA) measurement results sent from the sensors. It is shown that the accuracy of the DOA-based FG geolocation algorithm can be improved by introducing approximated expressions for the mean and variance of the tangent and cotangent functions based on the first-order Taylor series (TS) at the tangent factor nodes of the FG. This paper also derives a closed-form expression of the Cramer-Rao lower bound (CRLB) for DOA-based geolocation, where the number of samples is taken into account. The proposed technique does not require high computational complexity because only mean and variance are to be exchanged between the nodes in the FG. It is shown that the position estimation accuracy with the proposed technique outperforms the conventional DOA-based least square (LS) technique and that the achieved root mean square error (RMSE) is very close to the theoretical CRLB. [ABSTRACT FROM AUTHOR]

Sparse-aware (SA) detectors have attracted a lot attention due to its significant performance and low-complexity, in particular for large-scale multiple-input multiple-output (MIMO) systems. Similar to the conventional multiuser detectors, the nonlinear or compressive sensing based SA detectors provide the better performance but are not appropriate for the overdetermined multiuser MIMO systems in sense of power and time consumption. The linear SA detector provides a more elegant tradeoff between performance and complexity compared to the nonlinear ones. However, the major limitation of the linear SA detector is that, as the zero-forcing or minimum mean square error detector, it was derived by relaxing the finite-alphabet constraints, and therefore its performance is still sub-optimal. In this paper, we propose a novel SA detector, named single-dimensional search-based SA (SDSB-SA) detector, for overdetermined uplink MIMO systems. The proposed SDSB-SA detector adheres to the finite-alphabet constraints so that it outperforms the conventional linear SA detector, in particular, in high SNR regime. Meanwhile, the proposed detector follows a single-dimensional search manner, so it has a very low computational complexity which is feasible for light-ware Internet of Thing devices for ultra-reliable low-latency communication. Numerical results show that the the proposed SDSB-SA detector provides a relatively better tradeoff between the performance and complexity compared with several existing detectors. [ABSTRACT FROM AUTHOR]

Wireless sensors rely on battery power, and in many applications it is difficult or prohibitive to replace them. Hence, in order to prolongate the system's lifetime, some sensors can be kept inactive while others perform all the tasks. In this paper, we study the k-coverage problem of activating the minimum number of sensors to ensure that every point in the area is covered by at least k sensors. This ensures higher fault tolerance, robustness, and improves many operations, among which position detection and intrusion detection. The k-coverage problem is trivially NP-complete, and hence we can only provide approximation algorithms. In this paper, we present an algorithm based on an extension of the classical e-net technique. This method gives an O(logM)-approximation, where M is the number of sensors in an optimal solution. We do not make any particular assumption on the shape of the areas covered by each sensor, besides that they must be closed, connected, and without holes. [ABSTRACT FROM AUTHOR]

Multiple-mode orthogonal frequency division multiplexing with index modulation (MM-OFDM-IM) is a promising multiple-carrier modulation technique, which activates all OFDM subcarriers to transmit multiple distinguishable modes (or signal constellations, alternatively) and carries additional information with the full permutation of the modes, enjoying superior error performance and high spectral efficiency. In this paper, we propose a novel detector tailored to MM-OFDM-IM based on the expectation maximization (EM) algorithm to further improve the detection performance. The idea is to first select the signal constellation points candidates for any given mode permutation by applying the EM algorithm and then determine the most-likely mode permutation as well as the associated signal constellation points by minimizing the Euclidean distance. The effectiveness of the EM detector has been verified in terms of bit error rate (BER) performance by Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

A V-band receiver using a MHMIC multiport circuit is presented in this paper. The millimeterwave frequency conversion is performed using a passive circuit, the multiport, and related power detectors, avoiding the conventional millimeter-wave active costly mixers. Basically, the multiport circuit is an additive mixer in which the resulting sum of millimeter-wave signals is nonlinearly processed using millimeter-wave power detectors. This multiport heterodyne receiver is an excellent candidate for the future low-cost high-speed millimeter-wave wireless communication systems. The operating principle of the proposed heterodyne receiver and demodulation results of high-speed MPSK/QAM signals are presented and discussed in this paper. According to suggested datarate of 100-400 Mbps used to prove the operating principle, the IF of this receiver was chosen at 900 MHz. Therefore, this receiver is a possible alternative solution for WPAN applications [ABSTRACT FROM AUTHOR]

In order to improve the energy efficiency of cognitive wireless sensor network, this paper introduces threshold-sensitive energy efficient sensor network (TEEN) routing protocol into cognitive wireless sensor network. To make routing and spectrum more stable, this paper presents advanced threshold-sensitive energy efficient sensor network (A-TEEN), which is the optimization of TEEN. A-TEEN optimized the cluster head election method compared with TEEN. Simulation result shows that compared with low-energy adaptive clustering hierarchy, TEEN increases the energy efficiency and extends life cycle of cognitive wireless sensor network. A-TEEN improves the energy efficiency and lifetime further operating in the same settings compared with the regular TEEN. [ABSTRACT FROM AUTHOR]

This paper is concerned with distributed estimation of a scalar parameter using a wireless sensor network (WSN) that employs a large number of sensors operating under limited bandwidth resource. A semi-orthogonal multiple-access (MA) scheme is proposed to transmit observations from K sensors to a fusion center (FC) via N orthogonal channels, where K≥ N. The K sensors are divided into N groups, where the sensors in each group simultaneously transmit on one orthogonal channel (and hence the transmitted signals are directly superimposed at the FC as opposed to be coherently combined). Under such a semi-orthogonal multiple access channel (MAC), performance of the linear minimum mean squared error (LMMSE) estimation is analyzed in terms of two indicators: the channel noise suppression capability and the observation noise suppression capability. The analysis is performed for two versions of the proposed semi-orthogonal MA scheme: fixed sensor grouping and adaptive sensor grouping. In particular, the semi-orthogonal MAC with fixed sensor grouping is shown to have the same channel noise suppression capability and two times the observation noise suppression capability when compared to the orthogonal MAC under the same bandwidth resource. For the semi-orthogonal MAC with adaptive sensor grouping, it is determined that N=4 is the most favorable number of orthogonal channels when taking into account both performance and feedback requirement. In particular, the semi-orthogonal MAC with adaptive sensor grouping is shown to perform very close to that of the hybrid MAC, while requiring only log2 N=2 bits of information feedback instead of the exact channel phase for each sensor. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), it is often necessary to update the software running on sensors, which requires reliable dissemination of large data objects to each sensor with energy efficiency. During data dissemination, due to sleep scheduling designed for energy efficiency, some sensors may not receive some packets at some time slots. In the meantime, due to the unreliability of wireless communication, a sensor may not successfully receive a packet even when it is in the active mode. Thus, retransmission of such packets to those sensors is necessary, which consumes more energy and increases the delay of data dissemination cycle. In this paper, we propose a network coding-based approach in data dissemination such that data dissemination can be accomplished at the earliest time. Thus, less energy is consumed and the delay can be decreased. The impact of packet loss probability and the sleep probability of sensors on the network coding gain is analyzed. A threshold is also given to decide whether the current sleep scheduling is effective on energy saving in data dissemination process or not. Simulation results demonstrate the effectiveness and scalability of the proposed work. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), there generally exist many different objective functions to be optimized. In this paper, we propose a stochastic multiobjective optimization approach to solve such kind of problem. We first formulate a general multiobjective optimization problem. We then decompose the optimization formulation through Lagrange dual decomposition and adopt the stochastic quasigradient algorithm to solve the primal-dual problem in a distributed way. We show theoretically that our algorithm converges to the optimal solution of the primal problem by using the knowledge of stochastic programming. Furthermore, the formulation provides a general stochastic multiobjective optimization framework for WSNs. We illustrate how the general framework works by considering an example of the optimal rate allocation problem in multipath WSNs with time-varying channel. Extensive simulation results are given to demonstrate the effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

Recent advances in embedded microsensing technologies and low-energy cost sensors have made wireless sensor networks possible. Object tracking is an important research of wireless sensor networks. However, most object tracking tree is constructed based on a predefined mobility profile. When the real object movement behaviors are very different to the predefined mobility profile, the object tracking tree performance will become worse. In the paper, we will propose a dynamic adaptation mechanism, referred to as "Message-Tree Adaptive (MTA)" procedure, to improve the object tracking tree when the predefined mobility profiles do not match. From the simulation results, the performance of the object tracking tree can be significantly improved, when the MTA procedure is performed. [ABSTRACT FROM AUTHOR]

Fault-tolerant target detection and localization is a challenging task in collaborative sensor networks. This paper introduces our exploratory work toward identifying the targets in sensor networks with faulty sensors. We explore both spatial and temporal dimensions for data aggregation to decrease the false alarm rate and improve the target position accuracy. To filter out extreme measurements, the median of all readings in a close neighborhood of a sensor is used to approximate its local observation to the targets. The sensor whose observation is a local maxima computes a position estimate at each epoch. Results from multiple epoches are combined together to further decrease the false alarm rate and improve the target localization accuracy. Our algorithms have low computation and communication overheads. Simulation study demonstrates the validity and efficiency of our design. [ABSTRACT FROM AUTHOR]

Sensor networks require a simple and efficient medium access control policy achieving high system throughput with no or limited control overhead in order to increase the network lifetime by minimizing the energy consumed during transmission attempts. Time-spread multiple-access (TSMA) policies that have been proposed for ad hoc network environments, can also be employed in sensor networks, since no control overhead is introduced. However, they do not take advantage of any cross-layer information in order to exploit the idiosyncrasies of the particular sensor network environment such as the presence of typically static nodes and a common destination for the forwarded data. An adaptive probabilistic TSMA-based policy, that is proposed and analyzed in this paper, exploits these idiosyncrasies and achieves higher system throughput than the existing TSMA-based policies without any need for extra control overhead. As it is analytically shown in this paper, the proposed policy always outperforms the existing TSMA-based policies, if certain parameter values are properly set; the analysis also provides for these proper values. It is also shown that the proposed policy is characterized by a certain convergence period and that high system throughput is achieved for long convergence periods. The claims and expectations of the provided analysis are supported by simulation results presented in this paper. [ABSTRACT FROM AUTHOR]

Maximum-likelihood (ML) detection is guaranteed to yield minimum probability of erroneous detection and is thus of great importance for both multiuser detection and space-time decoding. For multiple-input multiple-output (MIMO) antenna systems where the number of receive antennas is at least the number of signals multiplexed in the spatial domain, ML detection can be done efficiently using sphere decoding. Suboptimal detectors are also well known to have reasonable performance at low complexity. It is, nevertheless, much less understood for obtaining good detection at affordable complexity if there are less receive antennas than transmitted signals (i.e., underdetermined MIMO systems). In this paper, our aim is to develop an effcient detection strategy that can achieve near ML performance for underdetermined MIMO systems. Our method is based on the geometrical understanding that the ML point happens to be a point that is "close" to the decoding hyperplane in all directions. The fact that such proximityclose points are much less is used to devise a decoding method that promises to greatly reduce the decoding complexity while achieving near ML performance. An average-case complexity analysis based on Gaussian approximation is also given. [ABSTRACT FROM AUTHOR]

In today's scenario, the fastest algorithm which uses a single layer of convolutional network to detect the objects from the image is single shot multi-box detector (SSD) algorithm. This paper studies object detection techniques to detect objects in real time on any device running the proposed model in any environment. In this paper, we have increased the classification accuracy of detecting objects by improving the SSD algorithm while keeping the speed constant. These improvements have been done in their convolutional layers, by using depth-wise separable convolution along with spatial separable convolutions generally called multilayer convolutional neural networks. The proposed method uses these multilayer convolutional neural networks to develop a system model which consists of multilayers to classify the given objects into any of the defined classes. The schemes then use multiple images and detect the objects from these images, labeling them with their respective class label. To speed up the computational performance, the proposed algorithm is applied along with the multilayer convolutional neural network which uses a larger number of default boxes and results in more accurate detection. The accuracy in detecting the objects is checked by different parameters such as loss function, frames per second (FPS), mean average precision (mAP), and aspect ratio. Experimental results confirm that our proposed improved SSD algorithm has high accuracy. [ABSTRACT FROM AUTHOR]

The interferences coming from the radar members degrade the detection and recognition performance of the radar sensor networks (RSNs) if the waveforms of the radar members are nonorthogonal. In this paper, we analyze the interferences by exploring the polarization information of the electromagnetic (EM) waves. Then, we propose the oblique projection polarization filtering- (OPPF-) based scheme to suppress the interferences while keeping the amplitude and phase of its own return in RSNs, even if the polarized states of the radar members are not orthogonal. We consider the cooperative RSNs environment where the polarization information of each radar member is known to all. The proposed method uses all radar members' polarization information to establish the corresponding filtering operator. The Doppler-shift and its uncertainty are independent of the polarization information, which contributes that the interferences can be suppressed without the utilization of the spatial, the temporal, the frequency, the time-delay and the Doppler-shift information. Theoretical analysis and the mathematical deduction show that the proposed scheme is a valid and simple implementation. Simulation results also demonstrate that this method can obtain a good filtering performance when dealing with the problem of interference suppressions for RSNs. [ABSTRACT FROM AUTHOR]

The evaluation of MAC protocols for Wireless Sensor Networks (WSNs) is often performed through simulation. These simulations necessarily abstract away from reality in many ways. However, the impact of these abstractions on the results of the simulations has received only limited attention. Moreover, many studies on the accuracy of simulation have studied either the physical layer and per link effects or routing protocol effects. To the best of our knowledge, no other work has focused on the study of the simulation abstractions with respect to MAC protocol performance. In this paper, we present the results of an experimental study of two often used abstractions in the simulation of WSN MAC protocols. We show that a simple SNR-based reception model can provide quite accurate results for metrics commonly used to evaluate MAC protocols. Furthermore, we provide an analysis of what the main sources of deviation are and thereby how the simulations can be improved to provide even better results. [ABSTRACT FROM AUTHOR]

The fact that the IEEE 802.15.4 MAC does not fully satisfy the strict wireless body sensor network (BSN) requirements in healthcare systems highlights the need for the design and analysis of new scalable MAC solutions, which guarantee low power consumption to all specific sorts of body sensors and traffic loads. While taking the challenging healthcare requirements into account, this paper aims for the study of energy consumption in BSN scenarios. For that purpose, the IEEE 802.15.4 MAC limitations are first examined, and other potential MAC layer alternatives are further explored. Our intent is to introduce energy-aware radio activation polices into a high-performance distributed queuing medium access control (DQ-MAC) protocol and evaluate its energy-saving achievements, as a function of the network load and the packet length. To do so, a fundamental energy-efficiency theoretical analysis for DQ-MAC protocols is hereby for the first time provided. By means of computer simulations, its performance is validated using IEEE 802.15.4 MAC system parameters. [ABSTRACT FROM AUTHOR]

As a multimedia information acquisition and processing method, wireless multimedia sensor network(WMSN) has great application potential in military and civilian areas. Compared with traditional wireless sensor network, the routing design of WMSN should obtain more attention on the quality of transmission. This paper proposes an adaptive reliable routing based on clustering hierarchy named ARCH, which includes energy prediction and power allocation mechanism. To obtain a better performance, the cluster structure is formed based on cellular topology. The introduced prediction mechanism makes the sensor nodes predict the remaining energy of other nodes, which dramatically reduces the overall information needed for energy balancing. ARCH can dynamically balance the energy consumption of nodes based on the predicted results provided by power allocation. The simulation results prove the efficiency of the proposed ARCH routing. [ABSTRACT FROM AUTHOR]

This paper revisits the problem of Quality of Service (QoS) provisioning to assess the relevance of using multipath routing to improve the reliability and packet delivery in wireless sensor networks while maintaining lower power consumption levels. Building upon a previous benchmark, we propose a traffic engineering model that relies on delay, reliability, and energy-constrained paths to achieve faster, reliable, and energy-efficient transmission of the information routed by a wireless sensor network. As a step forward into the implementation of the proposed QoS model, we describe the initial steps of its packet forwarding protocol and highlight the tradeoff between the complexity of the model and the ease of implementation. Using simulation, we demonstrate the relative efficiency of our proposed model compared to single path routing, disjoint path routing, and the previously proposed benchmarks. The results reveal that by achieving a good tradeoff between delay minimization, reliability maximization, and path set selection, our model outperforms the other models in terms of energy consumption and quality of paths used to route the information. [ABSTRACT FROM AUTHOR]

Most wireless sensor networks (WSNs) consist of battery-powered nodes and are limited to hundreds of nodes. Battery replacement is a very costly operation and a key factor in limiting successful large-scale deployments. The recent advances in both energy harvesters and low-power communication systems hold promise for deploying large-scale wireless green-powered sensor networks (WGSNs). This will enable new applications and will eliminate environmentally unfriendly battery disposal. This paper explores the use of energy harvesters to scavenge power for nodes in a WSN. The design and implementation of a generic energy-harvesting framework, suited for a WSN simulator as well as a real-life testbed, are proposed. These frameworks are used to evaluate whether a carrier sensemultiple access with collision avoidance scheme is sufficiently reliable for use in emerging large-scale energy harvesting electronic shelf label (EHESL) systems (i.e., 12000 labels in a star topology). Both the simulator and testbed experiments yielded an average success rate up to 92%, with an arrival rate of 40 transceive cycles per second. We have demonstrated that our generic energy-harvesting framework is useful for WGSN research because the simulator allowed us to verify the achieved results on the real-life testbed and vice versa. [ABSTRACT FROM AUTHOR]

Ultra-wideband impulse radio (UWB-IR) sensor network has intensive military and commercial applications. However, the interference between UWB and other existed networks should be casually investigated. In this paper, we consider interference mitigation in UWB sensors in the context of cognitive radio (CR). Firstly, we suggest a general state transitionmodel to characterize the working states evolution of legal networks, also referred to as primary users (PU). Spectrum sensing, used to identify the state of PU, is formulated as detection of a corresponding state sequence. Maximum posterior probability (MAP) criterion is adopted to perform spectrum sensing. By exploring potential gain of state transitions, detection probability for nearby networks is improved significantly. Subsequently, based on the radius basis function neural network (RBF), we present a novel spectrum sculptor to design UWB waveforms. Attributed to the excellent reconfiguration of RBF, our scheme can produce UWB waveforms tracing available spectrums. The designed waveforms can entirely utilize multiple unoccupied bands to maintain uninterrupted communications. Also, sufficient spectral attenuation can be generated in specific bands to mitigate mutual interference between UWB sensors and other networks. Besides, orthogonal waveforms can be easily derived, which either improves transmission performance or provides a flexible accessing strategy for UWB sensors. [ABSTRACT FROM AUTHOR]

Sink mobility has attracted much research interest in recent years because it can improve network performance such as energy efficiency and throughput. An energy-unconscious moving strategy is potentially harmful to the balance of the energy consumption among sensor nodes so as to aggravate the hotspot problem of sensor networks. In this paper, we propose an autonomous moving strategy for the mobile sinks in data-gathering applications. In our solution, a mobile sink approaches the nodes with high residual energy to force them to forward data for other nodes and tries to avoid passing by the nodes with low energy. We performed simulation experiments to compare our solution with other three data-gathering schemes. The simulation results show that our strategy cannot only extend network lifetime notably but also provides scalability and topology adaptability. [ABSTRACT FROM AUTHOR]

Many works have been devoted to connectivity of ad hoc networks. This is an important feature for wireless sensor networks (WSNs) to provide the nodes with the capability of communicating with one or several sinks. In most of these works, radio links are assumed ideal, that is, with no transmission errors. To fulfil this assumption, the reception threshold should be high enough to guarantee that radio links have a low transmission error probability. As a consequence, all unreliable links are dismissed. This approach is suboptimal concerning energy consumption because unreliable links should permit to reduce either the transmission power or the number of active nodes. The aim of this paper is to quantify the contribution of unreliable long hops to an increase of the connectivity of WSNs. In our model, each node is assumed to be connected to each other node in a probabilistic manner. Such a network is modeled as a complete random graph, that is, all edges exist. The instantaneous node degree is then defined as the number of simultaneous valid single-hop receptions of the same message, and finally the mean node degree is computed analytically in both AWGN and block-fading channels. We show the impact on connectivity of two MACs and routing parameters. The first one is the energy detection level such as the one used in carrier sense mechanisms. The second one is the reliability threshold used by the routing layer to select stable links only. Both analytic and simulation results show that using opportunistic protocols is challenging. [ABSTRACT FROM AUTHOR]

Minimizing the communication overhead is always a hot topic in wireless sensor networks. In a multirate query system, data sources disseminate the data streams to users at the frequency they request. However, sending data in different frequencies to individual users is very costly. We address this problem by broadcasting a single consolidated data stream, aiming at reducing the amount of transmitted data. Taking into account the data correlation, we can reconstruct the data streams at lower frequencies from the consolidated stream at a higher frequency. In this paper, we propose an energy-efficient framework to process multirate queries and investigate the path-sharing routing tree construction method together with the rate conversion mechanism. We evaluate both the accuracy and energy efficiency by simulation. Simulation results indicate that with a reasonable level of tolerance, the performance gain is significant. As far as we know, this is the first energy-efficient solution formultirate query in wireless sensor networks. [ABSTRACT FROM AUTHOR]

A critical issue in battery-powered sensor networks is to construct energy efficient virtual backbones for network routing. Recent study in battery technology reveals that batteries tend to discharge more power than needed and reimburse the over-discharged power if they are recovered. In this paper we first provide a mathematical battery model suitable for implementation in sensor networks. We then introduce the concept of battery-aware connected dominating set (BACDS) and show that in general the minimum BACDS (MBACDS) can achieve longer lifetime than the previous backbone structures. Then we show that finding a MBACDS is NP-hard and give a distributed approximation algorithm to construct the BACDS. The resulting BACDS constructed by our algorithm is at most (8 + Δ) opt size, where Δ is the maximum node degree and opt is the size of an optimal BACDS. Simulation results show that the BACDS can save a significant amount of energy and achieve up to 30% longer network lifetime than previous schemes. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, distributed timing synchronization based on pulse-coupled oscillators at the physical layer is currently being investigated as an interesting alternative to packet synchronization. In this paper, the convergence properties of such a system are studied through algebraic graph theory, by modeling the nodes as discrete-time clocks. A general scenario where clocks may have different free-oscillation frequencies is considered, and both time-invariant and time-variant network topologies (or fading channels) are discussed. Furthermore, it is shown that the system of oscillators can be studied as a set of coupled discrete-time PLLs. Based on this observation, a generalized system design is discussed, and it is proved that known results in the context of conventional PLLs for carrier acquisition have a counterpart in distributed systems. Finally, practical details of the implementation of the distributed synchronization algorithm over a bandlimited noisy channel are covered. [ABSTRACT FROM AUTHOR]

Owing to numerous potential applications, wireless sensor networks have been attracting significant research effort recently. The critical challenge that wireless sensor networks often face is to sustain long-term operation on limited battery energy. Coverage maintenance schemes can effectively prolong network lifetime by selecting and employing a subset of sensors in the network to provide sufficient sensing coverage over a target region. We envision future wireless sensor networks composed of a vast number of miniaturized sensors in exceedingly high density. Therefore, the key issue of coverage maintenance for future sensor networks is the scalability to sensor deployment density. In this paper, we propose a novel coverage maintenance scheme, scalable coverage maintenance (SCOM), which is scalable to sensor deployment density in terms of communication overhead (i.e., number of transmitted and received beacons) and computational complexity (i.e., time and space complexity). In addition, SCOM achieves high energy efficiency and load balancing over different sensors. We have validated our claims through both analysis and simulations. [ABSTRACT FROM AUTHOR]

This paper deals with a novel MAC layer protocol, namely, directive synchronous transmission asynchronous reception (D-STAR) able to space-time synchronize a wireless sensor network (WSN). To this end, D-STAR integrates directional antennas within the communications framework, while taking into account both sleep/active states, according to a cross-layer design. After characterizing the D-STAR protocol in terms of functional characteristics, the related performance is presented, in terms of network lifetime gain, setup latency, and collision probability. It has shown a remarkable gain in terms of energy consumption reduction with respect to the basic approach endowed with omnidirectional antennas, without increasing the signaling overhead nor affecting the setup latency. [ABSTRACT FROM AUTHOR]

Finding the minimum connected dominating set (MCDS) is a key problem in wireless sensor networks, which is crucial for efficient routing and broadcasting. However, the MCDS problem is NP-hard. In this paper, a new approximation algorithm with approximation ratio H(Δ)+3 in time O(n2) is proposed to approach the MCDS problem. The key idea is to divide the sensors in CDS into core sensors and supporting sensors. The core sensors dominate the supporting sensors in CDS, while the supporting sensors dominate other sensors that are not in CDS. To minimize the number of both the cores and the supporters, a three-phased algorithm is proposed. (1) Finding the base-core sensors by constructing independent set (denoted as S1), in which the sensors who have the largest |N2(v)||N(v)| (number of two-hop neighbors over the number of one-hop neighbors) will be selected greedily into S1; (2) Connecting all base-core sensors in S1 to form a connected subgraph, the sensors in the subgraph are called cores; (3) Adding the one-hop neighbors of the core sensors to the supporter set S2. This guarantees a small number of sensors can be added into CDS, which is a novel scheme for MCDS construction. Extensive simulation results are shown to validate the performance of our algorithm. [ABSTRACT FROM AUTHOR]

The target detection model based on convolutional neural networks has recently achieved a series of exciting results in the target detection tasks of the PASCAL VOC and MS COCO data sets. However, limited by the data set for a particular scenario, some techniques or models applied to the actual environment are often not satisfactory. Based on cluster analysis and deep neural network, this paper proposed a new Statistic Experience-based Adaptive One-shot Network (SENet). The whole model solved the following practical problems. (1) By clustering the existing image classification dataset ImageNet, a common set of target detection datasets is formed, and a data set named ImageNet iLOC is formed to solve the object detection. The problem of single and insufficient quantities in the task. (2) We use cluster analysis on the size and shape of objects in each sample, which solves the problem of inaccurate manual selection of suggested areas during object detection. (3) In the multi-resolution training and prediction process, we reasonably allocate the size and shape of the suggested frame at different resolutions, greatly improve the utilization rate of the proposed frame, reduce the calculation amount of the model, and further improve the real-time performance of the model. The experimental results show that the model has a breakthrough in accuracy and speed (FPS reaches 54 in the case of a 3.4% increase in mAP). [ABSTRACT FROM AUTHOR]

Accurate localization of nodes is one of the key issues of wireless sensor network (WSN). A localization algorithm using expected hop progress (LAEP) has been successfully applied in isotropic wireless sensor networks. However, range-free LAEP cannot be directly used for anisotropic WSNs because anisotropic problems limit the applicability of multi-hop localization. In order to solve the problem, an improved localization algorithm is proposed to reduce the localization error. In this paper, we adapt the expected hop progress to anisotropic WSNs by considering both hop count computation and anchor selection. Then, particle swarm optimization algorithm is introduced to improve the positioning accuracy. The experimental results demonstrate that our algorithm has better higher precision than do state-of-the-art algorithms. Even for isotropic WSNs, our algorithm always outperforms its counterparts. [ABSTRACT FROM AUTHOR]

The interest on applications where machine learning algorithms and communications are combined has been on a rising in recent years. Machine learning and neural networks are being advocated as a way of improving the performance of several functions across all layers of future communication systems. Furthermore, in applications where complexity reduction is essential for the system feasibility at the cost of an affordable performance loss, more efficient systems might be achieved with the aid of machine learning algorithms. Signal detection for multiple-input multiple-output (MIMO) systems has become a hot topic in recent years given its prominent role in fourth and fifth generations of mobile networks. However, the computational complexity in MIMO systems can become prohibitive when the number of antennas increases. Therefore, by leveraging neural networks architectures we propose a deep unfolded detector, whereby the algorithm of the probability data association (PDA) detector is adapted and enhanced by means of neural network learning capabilities. We unveil that the proposed detector is orders-of-magnitude less complex than the PDA detector, yet presenting no severe penalties in performance in terms of bit error rate (BER). [ABSTRACT FROM AUTHOR]

New algorithms and architectures in the context of 5G shall be explored to ensure the efficiency, robustness, and consistency in variable application environments which concern different issues, such as the smart grid, water supply, gas monitoring, etc. In power line monitoring, we can get lots of images through a wide range of sensors and can ensure the safe operation of the smart grid by analyzing images. Feature extraction is critical to identify insulators in the aerial image. Existing approaches have primarily addressed this problem by using a single type of feature such as color feature, texture feature, or shape feature. However, a single type of feature usually leads to poor classification rates and missed detection in identifying insulators. Aiming to fully describe the characteristics of insulator and enhance the robustness of insulator against the complex background in aerial images, we combine three types of feature including color feature, texture feature, and shape feature towards a multi-type feature. Then, the multi-type feature is integrated with k-nearest neighbor classifier for automatic classifying insulators. Our experiment with 4500 aerial images demonstrates that the recognition rate is 99% by using this multi-type feature. Comparing to a single type of feature, our method yielded a better classification performance. [ABSTRACT FROM AUTHOR]

To allow doctors to monitor the physical parameters of the patient’s body in real time and to understand the changes in the patient’s condition in time, the medical remote monitoring system based on the Internet of Things was studied. From the perspective of practical application of hospital wards, a medical health monitoring system was designed with the help of CC2430 microcontroller, human information sensor, and microelectronic and modern wireless communication technology. In addition, a sensor node circuit and a coordinator node circuit for collecting medical signals were designed. Meanwhile, the software of wireless sensor network was designed. Finally, the online debugging of each system module was combined with hardware and software. The experimental result proved that the network node was reliable and the data transmission was accurate. It is concluded that the medical monitoring system basically meets the design requirements of this paper. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) in the operating room can aid to improve the quality of the computer-aided surgical system. Patient-to-image registration is an important issue for computer-aided surgical systems. Automating the procedure of patient-to-image registration could increase tracking accuracy and lower the time consumed for performing the procedure of registration. Therefore, we propose an automatic registration method to address this issue by constructing a wireless sensor network system for surgery. A plastic fiducial object combing with specific markers is developed to perform registration in that the ultimate purpose is to integrate them into a surgical robotic system for surgical navigation. The specific markers are designed to localize the position of the small EM sensor and can be automatically detected in CT/MRI images by an automatic algorithm. The positions of the EM tracking sensors can be calibrated during the procedure of registration. Some experiments are designed and performed, and the experimental results demonstrate that the proposed registration method is robust and accurate. The proposed registration method is a foundational link of the surgical robots combing with virtual or augmented reality technology that all these technologies will be performed in further surgical navigation. [ABSTRACT FROM AUTHOR]

We address the problem of power allocation in a wireless sensor network where distributed sensors amplify and forward their partial and noisy observations of a Gaussian random source to a remote fusion center (FC). The FC reconstructs the source based on linear minimum mean-squared error (LMMSE) estimation rule. Motivated by the availability of limited energy in the sensor networks, we undertake the design of power allocation based on minimization of the reconstruction distortion subject to a constraint on the network transmit power. The design is based on the following two cases: (i) exact knowledge of the channel gains and (ii) the estimates of the channel gains. We show that the distortion can be represented as a convex function of the transmit powers of the sensors. Moreover, we show that the power allocation based on this distortion function does not bear any closed form solution. To this end, we propose a novel design based on the successive approximation of the LMMSE distortion, which turns out to be simple, computationally efficient, and exhibits excellent convergence properties. The simulation examples illustrate that the proposed design holds considerable performance gain compared to a uniform power allocation scheme. [ABSTRACT FROM AUTHOR]

We optimize the cluster structure to solve problems such as the uneven energy consumption of the radar sensor nodes and random cluster head selection in the traditional clustering routing algorithm. According to the defined cost function for clusters, we present the clustering algorithm which is based on radio-free space path loss. In addition, we propose the energy and distance pheromones based on the residual energy and aggregation of the radar sensor nodes. According to bionic heuristic algorithm, a new ant colony-based clustering algorithm for radar sensor networks is also proposed. Simulation results show that this algorithm can get a better balance of the energy consumption and then remarkably prolong the lifetime of the radar sensor network. [ABSTRACT FROM AUTHOR]

Based on the zero correlation zone (ZCZ) concept, we present the definitions and properties of a set of new ternary codes, ZCZ sequence-Pair Set (ZCZPS), and propose a method to use the optimized punctured sequence-pair along with Hadamard matrix to construct an optimized punctured ZCZ sequence-pair set (OPZCZPS) which has ideal autocorrelation and cross-correlation properties in the zero correlation zone. Considering the moving target radar system, the correlation properties of the codes will not be severely affected when Doppler shift is not large. We apply the proposed codes as pulse compression codes to radar system and the simulation results show that optimized punctured ZCZ sequence-pairs outperform other conventional pulse compression codes, such as the well-known polyphase code-P4 code. [ABSTRACT FROM AUTHOR]

We present a mathematical model to study decentralized detection in clustered wireless sensor networks (WSNs). Sensors and fusion centers (FCs) are distributed with the aim of detecting an event of interest. Sensors are organized in clusters, with FCs acting as cluster heads, and are supposed to observe the same common binary phenomenon. A query-based application is accounted for; FCs periodically send queries and wait for replies coming from sensors. After reception of data, FCs perform data fusion with a majority-like fusion rule and send their decisions to an access point (AP), where a final data fusion is carried out and an estimate of the phenomenon is obtained. We assume that sensors are IEEE 802.15.4-compliant devices and use the medium access control (MAC) protocol defined by the standard, based on carrier-sense multiple access with collision avoidance. Decentralized detection and MAC issues are jointly investigated through analytical modelling. The proposed framework allows the derivation of the probability of decision error at the AP, when accounting for packets' losses due to possible collisions. Our results show that MAC losses strongly affect system performance. The impact of different clustering configurations and of noisy communications is also investigated. [ABSTRACT FROM AUTHOR]

We consider the problem of minimizing transmission energy in wireless sensor networks by taking into account that every sensor may require a different bit rate and reliability according to its particular application. We propose a cross-layer approach to tackle such a minimization in centralized networks for the total transmission energy consumption of the network: in the physical layer, for each sensor the sink estimates the channel gain and adaptively selects a modulation scheme; in the MAC layer, each sensor is correspondingly assigned a number of time slots. The modulation level and the number of allocated time slots for every sensor are constrained to attain their applications bit rates in a global energy-efficient manner. The signal-to-noise ratio gap approximation is used in our exposition in order to jointly handle required bit rates, transmission energies, and symbol error rates. [ABSTRACT FROM AUTHOR]

We analyze the lifetime of clustered sensor networks with decentralized binary detection under a physical layer quality-of-service (QoS) constraint, given by the maximum tolerable probability of decision error at the access point (AP). In order to properly model the network behavior, we consider four different distributions (exponential, uniform, Rayleigh, and lognormal) for the lifetime of a single sensor. We show the benefits, in terms of longer network lifetime, of adaptive reclustering. We also derive an analytical framework for the computation of the network lifetime and the penalty, in terms of time delay and energy consumption, brought by adaptive reclustering. On the other hand, absence of reclustering leads to a shorter network lifetime, and we show the impact of various clustering configurations under different QoS conditions. Our results show that the organization of sensors in a few big clusters is the winning strategy to maximize the network lifetime. Moreover, the observation of the phenomenon should be frequent in order to limit the penalties associated with the reclustering procedure. We also apply the developed framework to analyze the energy consumption associated with the proposed reclustering protocol, obtaining results in good agreement with the performance of realistic wireless sensor networks. Finally, we present simulation results on the lifetime of IEEE 802.15.4 wireless sensor networks, which enrich the proposed analytical framework and show that typical networking performance metrics (such as throughput and delay) are influenced by the sensor network lifetime. [ABSTRACT FROM AUTHOR]

A modeling framework for the problem of deploying a set of heterogeneous sensors in a field with time-varying differential surveillance requirements is presented. The problem is formulated as mixed integer mathematical program with the objective to maximize coverage of a given field. Two metaheuristics are used to solve this problem. The first heuristic adopts a genetic algorithm (GA) approach while the second heuristic implements a simulated annealing (SA) algorithm. A set of experiments is used to illustrate the capabilities of the developed models and to compare their performance. The experiments investigate the effect of parameters related to the size of the sensor deployment problem including number of deployed sensors, size of the monitored field, and length of the monitoring horizon. They also examine several endogenous parameters related to the developed GA and SA algorithms. [ABSTRACT FROM AUTHOR]

Recent years have seen a significant growth in Internet of Things (IoT) technology consisting of a large number of devices embedded with sensors and deployed to perform monitoring and actuation tasks. The IoT devices collect large volumes of data that is usually uploaded to cloud to perform analytics and predictions. One of the main challenges in IoT is the transportation of large-scale data collected over a period of time at a remote site. Cellular networks are already facing explosive growth of mobile data traffic due to the proliferation of smart devices and traffic-intensive applications. An alternate solution is to perform the data offloading, where a portion of data can be offloaded from primary links and transferred using opportunistic terminal-to-terminal (T2T) network that relies on direct communication between mobile users, without any need for an infrastructure backbone. However, such approach may lead to data loss and delay if dynamics of time-varying topology and mobility of nodes is not taken care of. To address this challenge, we propose three prediction-based offloading schemes that exploit the mobility patterns and temporal contacts of nodes to predict future data transfer opportunities. We have utilized the High-level Petri Nets to model and formally analyzed the communication processes of the proposed schemes. The new symbolic model verifier (NuSMV) has been employed for the verification of the three schemes against the identified constraints. The verification results affirm the correctness and scalability of the models. The protocols are evaluated with performance metrics, such as the delivery ratio, latency, and overhead. Our results indicate significant improvement in performance compared to existing approaches. [ABSTRACT FROM AUTHOR]

Compared to linear precoding, Tomlinson-Harashima precoding (THP) requires less transmit power to eliminate the spatial interference in a multi-user downlink scenario involving a multi-antenna transmitter and geographically separated receivers. However, THP gives rise to certain performance losses, referred to as modulo loss and power loss. Based on the observation that part of the users can omit the modulo operation at the receiver during an entire frame, we present an alternative detector, which reduces the modulo loss compared to the conventional detector. In addition, this contribution compares several existing and novel algorithms for selecting the user ordering and the rotation of the constellations at the transmitter, to increase the SNR at the detector and decrease the modulo loss for the alternative detector. Compared to the better of linear precoding and THP with conventional detector, the optimized alternative detector achieves significant gains (up to about 4 dB) for terrestrial wireless communication, whereas smaller gains (up to about 1 dB) are obtained for multi-beam satellite communication. [ABSTRACT FROM AUTHOR]