Internet of Things (IoT) is one of the most cited terms within the communication research communities. Next generation wireless networks technologies are expected to have massive‐connections of tens of billions of devices. Such a huge number of devices raised a number of concerns in regards to how much accessible resources are available and what are the best technologies for managing those resources, all in order to avoid shutdowns/collapses in every means. In terms of wireless networks, and in regards to energy being the backbone of IoT devices, Low Power Wide Area Networks (LPWAN) technologies are considered to be a potential solution for IoT applications. In particular, this study reviews Long‐Range (LoRa) technology and advances in the literature of LoRaWAN protocol to date. Furthermore, it discusses the challenges in LoRaWAN and diverts the attention towards applying Ultra‐Dense Network concept on LPWAN.

Internet of Things (IoT) is one of the most cited terms within the wireless communication research communities. Next generation wireless networks technologies are expected to have massive-connections of tens of billions of devices. In terms of wireless networks, and in regards to collisions and transmission delay drawbacks being critical challenges when deploying IoT devices, Low Power Wide Area Networks (LPWAN) technologies are considered to be a potential solution for IoT applications. In particular, this paper investigates the use of Long-Range (LoRa) technology for serving dense applications. Furthermore, it identifies a dense application and investigates the possibility of using LoRaWAN for such applications. This work proposes a priority scheduling technique based on unsupervised learning clustering algorithm (K-Means). The proposed technique shows a reduction of the collision rate, the transmission delay and enhancement of the throughput in comparison to conventional LoRaWAN networks and other optimisation techniques.

The ZA-NLMS (for zero-attractor) represents arguably the seminal sparsity-aware gradient adaptive algorithm. As it is constraint by the ℓ1-norm of the filter weights, the underlying problem turns convex, hence with unique solution (in expected sense). Despite these friendly properties, the algorithm convergence and, more important, the best-performing sparsity tradeoff are yet to be effectively studied. This paper presents a comprehensive analytical study on ZA-NLMS' convergence, which results in the optimal (constant) sparsity tradeoff. The value of this decisive hyperparameter from a practitioner point of view turns out related to the 3/2-power of the adaptive filter length. This outcome, difficult to argue intuitively, as well as the convergence model, have been exhaustively validated with numerical experiments.

Embedding the norm in gradient-based adaptive filtering is a popular solution for sparse plant estimation. Even though the foundations are well understood, the selection of the sparsity hyper-parameter still remains today matter of study. Supported on the modal analysis of the adaptive algorithm near steady state, this paper shows that the optimal sparsity tradeoff depends on filter length, plant sparsity and signal-to-noise ratio. In a practical implementation, these terms are obtained with an unsupervised mechanism tracking the filter weights. Simulation results prove the robustness and superiority of the novel adaptive-tradeoff sparsity-aware method.

Energy efficiency is a crucial challenge in cluster-based capillary networks for Internet of Things (IoT) systems, where the cluster heads (CHs) selection has great impact on the network performance. It is an optimization problem to find the optimum number of CHs as well as which devices are selected as CHs. In this paper, we formulate the clustering problem into the CHs selection procedure with the aim of maximizing the average network lifetime in every round. In particular, we propose a novel CHs selection scheme based on QPSO and investigate how effective it is to prolong network lifetime and reserve the overall battery capacity. The simulation results prove that the proposed QPSO outperforms other evolutionary algorithms and can improve the network lifetime by almost 10%.

The ℓ0-normalized least mean squares (ℓ0-NLMS) is arguably the reference gradient adaptive algorithm for sparse system estimation. However, alike all sparse gradient adaptive algorithms, the ℓ0-NLMS performance is sensitive to the (adequate) selection of the tradeoff parameter. Highlighted in this letter, the existence of two convergence modes, linked to the negligible and to the significant taps, paves the way for the convergence analysis, which results in a set of nonlinear (quadratic) convergence equations. Therefrom, the minimization of the steady-state misalignment concludes in the optimal tradeoff, which happens to relate to the NLMS step size, filter length, plant sparsity, and noise level in an extremely compact fashion. Exhaustive simulation experiments show strong agreement between the analytical predictions and the empirical performance.

In order to exploit the inherent sparsity of an OFDM multipath wireless channel, this paper presents a novel estimation technique based on the ℓ1-LS criterion that relies on the selection of the optimal regularization parameter. This term is proven in the paper to be intimately related to the Signal to Noise Ratio (SNR) and Sparsity Degree (ρ) of the channel. Therefore, in order to take full advantage of the ℓ1-LS framework, an ad-hoc technique for estimating blindly the SNR and ρ of the problem is also introduced. The performance of the novel algorithm is compared to a number of reference techniques. Two standard measures, namely the Mean Square Error (MSE) of the estimated channel and the Symbol Error Rate (SER) of the OFDM system validate the robustness of the proposed algorithm. Indeed, this novel technique achieves better estimation along with better spectrum efficiency.

Embedding a sparse penalty in conventional Least Mean Square (LMS) adaptive algorithms is an established strategy to enhance the performance and robustness against noise in the estimation of sparse plants, such as wireless mul-tipath channels. In this paper we review the most prominent NLMS-based algorithms with ℓp-norm constraint, discussing the underlying mechanisms that lead to improvement gains in sparse scenarios. Simulation results validate the analysis and comparative discussion. Given that adaptive algorithms operating in time domain deteriorate with correlated signals, we propose hereby a novel frequency-domain (FD) ℓp-NLMS that performs in such situations. Simulation results indicate that the proposed method outperforms its time-domain counterparts not only in convergence rate but more importantly in residual misalignment. This important result has not been echoed so far.

This letter presents the exact normalized least-mean-square (NLMS) algorithm for the lp-norm-regularized square error, a popular choice for the identification of sparse systems corrupted by additive noise. The resulting exact lp-NLMS algorithm manifests differences to the original one, such as an independent update for each weight, a new sparsity-promoting compensated update, and the guarantee of stable convergence for any configuration (regardless the choice of lp norm and sparsity-tradeoff constant). Simulation results show that the exact lp-NLMS is stable and it outperforms the original one, thus validating the optimality of the proposed methodology.

Coverage of a camera sensor network (CSN) is a critical issue in deploying such network in maritime environment. In such environment, camera sensors will not be stationary after the initial deployment due to the hostile sea environment. Hence, the mobility pattern poses a major limitation on identifying the time-varying coverage. In full-view coverage, target's facing direction has been introduced to judge whether a target's facing direction is guaranteed to be captured. To this end, we exploit full-view coverage in CSN, since image shots at the frontal viewpoint of a given target considerably increase the possibility to detect and recognize the target. In this paper, we have developed a mobility pattern based on wind speed and spring relaxation technique which are used to characterize the total displacement of a given buoy. Furthermore, the average percentage of full-view coverage has been evaluated based on different parameters such as equilateral triangle grid length, sensing radius of camera, wind speed and wave height. Consequently, the proposed model has been validated through MATLAB simulation platform.

We consider a two-phase cooperative transmission strategy in cognitive femtocell networks, where a femto-base station (BS) employs cooperative relaying to increase throughput of a macro-mobile station (MS) at cell edge in reward for the access of the spectrum allocated to the macro-MS. Under this strategy, we formulate optimization problem to maximize the femto-MS rate while satisfying the rate requirement of macro-MS and illustrate the optimal policy: optimal time duration and power allocation. In addition, we analytically derive the critical cooperation region where a femto-BS can always achieve the rate requirement of the macro-MS, which is expressed in terms of distances between femto-BS and macro-MS and between macro-BS and femto-BS. The simulation results show the derived cooperation regions for different β and confirm rate gain of the cooperation strategy.

This paper proposes a multichannel medium access control (MAC) protocol for energy efficient IEEE802.15.4 home area networks that consists of novel allocation and superframe scheduling algorithm that improves the channel selection strategy and the superframe scheduling. It aims to improve the overall reliability and reduce the average delay. The proposed protocol is implemented in IEEE802.15.4 home area network andsimulation results show the proposed protocol outperforms the existing IEEE802.15.4 MAC protocols in the aspects of schedulability, reliability, delay and the overall throughput.

Nowadays, accurate and low-cost autonomous maritime surveillance of camera sensor network is a critical requirement for various applications. In a maritime environment, camera sensors are deployed on mobile platforms, such as buoys. With an anchored buoy architecture, sensor locations are quasi-stationary. When sensors move, some areas may not be covered at some times. This paper analyzes the extend of the uncovered area in a realistic maritime environment. We present numerical results to variation in uncovered areas from time to time. Also in this paper, the simulation of the uncovered area detection of camera sensor network (CSN) used in maritime surveillance is presented using MATLAB.

Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM), MIMO-OFDM, is considered as a spectrally efficient approach to achieve high throughput communications. This paper investigates the performance of MIMO-OFDM system using the proposed NLMS adaptive equalizer and the developed Singular Value Decomposition (SVD) technique in estimating the channel. The performances of using various values of the NLMS algorithm's step-size were also investigated and an optimum value, based on a trade-off between the convergence speed and the steady state noise floor, was chosen. Then, the bit error rate (BER) performance of the NLMS adaptive receiver is compared with that of the SVD algorithm. It is clear that the SVD gives better performance over the NLMS adaptive filter.

This paper gives an overview of the Long Term Evolution (LTE) of the Universal Mobile Telecommunication System (UMTS), which is being developed by the 3rd Generation Partnership Project (3GPP). LTE constitutes the latest step towards the 4th generation (4G) of radio technologies designed to increase the capacity and speed of mobile communications. Particular attention is given to the requirements and targets of LTE, its use of multiple antenna techniques, and to the Single Carrier Frequency Division Multiple Access (SC-FDMA) modulation scheme used in the LTE uplink. Furthermore new future research areas are proposed here.

The ongoing advancements in the multimedia technologies drive the need for efficient classification of the audio signals to make the content-based retrieval process more accurate and much easier from huge databases. The challenge of this task lies in an accurate extraction of signal characteristics so as to derive a strong discriminatory feature suitable for classification. In this paper, a time-frequency (TF) approach for audio classification is proposed. Audio signals are nonstationary in nature and TF approach is the best way to analyze them. The audio signals were decomposed using an adaptive TF decomposition algorithm, and the signal decomposition parameter based on octave (scaling) was used to generate a set of 42 features over three frequency bands within the auditory range. These features were analyzed using linear discriminant functions and classified into six music groups (rock, classical, country, jazz, folk and pop). Overall classification accuracies as high as 97.6 % was achieved by linear discriminant analysis of 170 audio signals.

We present a new and simple method for combining constellation shaping and bit-interleaved coded modulation over Gaussian channels. By considering the example of a 3-bit/dim turbo coding scheme using a 16-PAM constellation, it is shown that this technique can provide shaping gains of 0.7 dB, and that error performance within 1.6 dB of the continuous-input channel capacity limit is achieved.

In multiple input multiple output systems (MIMO), the increase in the number of channel parameters, renders less reliable conventional training based schemes. For a reasonable performance, conventional training based methods require a larger number of training symbols which reduces system bandwidth efficiency. In this paper, a semiblind channel estimation and tracking technique is proposed for concatenated turbo codes (TC) with space-time and frequency block codes (STFBC) over time-varying Rayleigh fading channels. The paper describes an effective approach to reduce the number of training symbols to half by implementing the Kalman filter (KF) with the constant modulus algorithm (CMA). Computer simulations show good estimation and tracking by the Kalman filter and CMA with only few training symbols (less than 7% of bandwidth efficiency loss) even when signals experience deep fades. The proposed method is shown to obtain fast channel estimation and tracking which is a requirement in fast fading channels.

The ongoing advancements in the multimedia technologies drive the need for efficient classification of the audio signals to make the content-based retrieval process more accurate and much easier from huge databases. The challenge of this task lies in an accurate extraction of signal characteristics so as to derive a strong discriminatory feature suitable for retrieval process. A time-frequency approach for audio classification is proposed. The audio signals were decomposed using an adaptive time-frequency decomposition algorithm, and the signal decomposition parameter octave (scale) was used to create patterns based on a similarity measure of the audio signals. These patterns were used to generate templates to classify the audio signals into different categories. Initial studies have yielded a overall correct classification accuracy of 90% with a database of 64 audio segments.