Recursive least squares identification methods for multivariate pseudo-linear systems using the data filtering

This paper concerns the parameter identification methods of multivariate pseudo-linear autoregressive systems. A multivariate recursive generalized least squares algorithm is presented as a comparison. By using the data filtering technique, a multivariate pseudo-linear autoregressive system is transformed into a filtered system model and a filtered noise model, and a filtering based multivariate recursive generalized least squares algorithm is developed for estimating the parameters of these two models. The proposed algorithm achieves a higher computational efficiency than the multivariate recursive generalized least squares algorithm, and the simulation results prove that the proposed method is effective.     

Optimal energy-delay tradeoff for opportunistic spectrum access in cognitive radio networks

Cognitive radio (CR) has been considered as a promising technology to enhance spectrum efficiency via opportunistic transmission at link level. Basic CR features allow secondary users (SUs) to transmit only when the licensed channel is not occupied by primary users (PUs). However, waiting for an idle time slot may lead to large packet delays and high energy consumption. We further consider that the SU may decide, at any moment, to use another dedicated way of communication (4G) in order to transmit his packets. Thus, we consider an Opportunistic Spectrum Access (OSA) mechanism that takes into account packet delay and energy consumption. We formulate the OSA problem as a Partially Observable Markov Decision Process (POMDP) by explicitly considering the energy consumption as well as packets’ delay, which are often ignored in existing OSA solutions. Specifically, we consider a POMDP with an average reward criterion. We derive structural properties of the value function and we show the existence of optimal strategies in the class of the threshold strategies. For implementation purposes, we propose online learning mechanisms that estimate the PU activity and determine the appropriate threshold strategy on the fly. In particular, numerical illustrations validate our theoretical findings.     

REBTAM: reliable energy balance traffic aware data reporting algorithm for object tracking in multi-sink wireless sensor networks

Recently, Multi-sink Wireless Sensor Networks (WSNs) have received more and more attention due to their significant advantages over the single sink WSNs such as improving network throughput, balancing energy consumption, and prolonging network lifetime. Object tracking is regarded as one of the key applications of WSNs due to its wide real-life applications such as wildlife animal monitoring and military area intrusion detection. However, many object tracking researches usually focus on how to track the location of objects accurately, while few researches focus on data reporting. In this work, we propose an efficient data reporting method for object tracking in multi-sink WSNs. Due to the limited energy resource of sensor nodes, it seems especially important to design an energy efficient data reporting algorithm for object tracking in WSNs. Moreover, the reliable data transmission is an essential aspect that should be considered when designing a WSN for object tracking application, where the loss of data packets will affect the accuracy of the tracking and location estimation of a mobile object. In addition, congestion in WSNs has negative impact on the performance, namely, decreased throughput, increased per-packet energy consumption and delay, thus congestion control is an important issue in WSNs. Consequentially, this paper aims to achieve both minimum energy consumption in reporting operation and balanced energy consumption among sensor nodes for WSN lifetime extension. Furthermore, data reliability is considered in our model where the sensed data can reach the sink node in a more reliable way. Finally, this paper presents a solution that sufficiently exerts the underloaded nodes to alleviate congestion and improve the overall throughput in WSNs. This work first formulates the problem as 0/1 Integer Linear Programming problem, and proposes a Reliable Energy Balance Traffic Aware greedy Algorithm in multi-sink WSNs (REBTAM) to solve the optimization problem. Through simulation, the performance of the proposed approach is evaluated and analyzed compared with the previous work which is related to our topic such as DTAR, NBPR, and MSDDGR protocols.     

Capacity and Channel Coding for Wireless Point-to-Point <Emphasis Type="Italic">Z</Emphasis><Superscript>2</Superscript> Channel

In this paper, we derive the capacity of the asymmetric \({\text{Z}}^{2}\)-channel, which has been presented for the first time as an optimization problem. Similar to the Z-Channel, the proposed \({\text{Z}}^{2}\)-channel can be modelled as a practical interference wireless channel. In addition, the capacity behavior of \({\text{Z}}^{2}\)-channel is discussed and some examples and simulation results for the capacity is presented. Also a code plan has been applied for \({\text{Z}}^{2}\)-channel, based on repetition code to simulate its performance and compare it with the original Z-channel. In conclusion, simulation results show that the \({\text{Z}}^{2}\)-channel can be used widely for different operating points.     

Design of Probability Density Function Targeting Energy Efficient Network for Coalition Based WSNs

Energy consumption is one of the important issues in wireless sensor network that rely on non chargeable batteries for power. Also, the sensor network has to maintain a desired sensing coverage area along with periodically sending of the sensed data to the base station. Therefore, coverage and the lifetime are the two important issues that need to be addressed. Effective deployment of wireless sensors is a major concern as the coverage and lifetime of any wireless sensor network depends on it. In this paper, we propose the design of a Probability Density Function (PDF) targeting the desired coverage, and energy efficient node deployment scheme. The suitability of the proposed PDF based node distribution to model the network architecture considered in this work has been analyzed. The PDF divides the deployment area into concentric coronas and provides a probability of occurrence of a node within any corona. Further, the performance of the proposed PDF is evaluated in terms of the coverage, the number of transmissions of packets and the lifetime of the network. The scheme is compared with the existing node deployment schemes based on various distributions. The percentage gain of the proposed PDF based node deployment is 32\(\%\) more than that when compared with the existing schemes. Thus, the simulation results obtained confirm the schemes superiority over the other existing schemes.     

Decentralized Coordination Control for a Network of Mobile Robotic Sensors

A decentralized control approach is presented for a network of robotic sensors to achieve the global coordination: group aggregation or consensus flocking. Each robotic sensor obeys three local interaction rules: group cohesion, collision avoidance and velocity alignment. Accordingly, the resultant control law for each robotic sensor is composed of three associated control components. The cohesion control component is based on geometry and additionally enables maintaining initial interconnection of network. The alignment control component ensures that all members in an interconnected network eventually reach to a common velocity. The control component for collision avoidance among members, as a passive role, is embedded in cohesion control. Simulation results demonstrate the capability of the proposed control approach in achieving the coordination behaviour with an initially interconnected network. However, we observed that an initially non-interconnected network may also achieve the coordination behaviour depending on the initial distribution and velocities. Initial interconnection of a network is only a sufficient but not essential condition for the network to achieve the coordination behaviour for our control approach.     

The Reliable Facility Location Problem Under Random Disruptions

The classical location models implicitly assume that the facilities, once built, will always operate as planned. However, some of the facilities may become unavailable from time to time due to disruptions caused by natural disasters, key-supplier bankruptcy, terrorist attacks, or labor strikes. Therefore, supply chain disruptions have gained considerable attention by researchers and practitioners in the past few decades. The catastrophic accidents highlighted the urgent need for effective solutions to manage supply chain disruptions in spite of their low probability of occurrence. Therefore, it is critical to take account of disruptions when designing supply chain networks so that they perform well as a whole even after a disruption. Taking into account the random disruption risk, this paper proposes a mixed integer programming model for the reliable facility location problem. This model try to minimize the sum of initial facility construction costs and expected transportation costs in both the regular and the failure scenarios, and present a Lagrange relaxation algorithm to solve it. This paper also analyzes the algorithm’s solving performance by a numerical experiment.     

Novel Fault-Tolerant Decompression Method of Corrupted Huffman Files

Data compression and decompression have been widely applied in modern communication and data transmission fields. But how to decompress corrupted lossless compressed files is still a challenge. This paper presents a fault-tolerant decompression (FTD) method for corrupted Huffman files. It is achieved by utilizing source prior information and heuristic method. In this paper, we propose to use Huffman coding rules and grammar rules to model the source prior. According to the source prior information, we can roughly estimate the range of error bits. As for error correction, a heuristic algorithm is developed to determine the accurate positions of error bits and correct the errors. The experimental results demonstrate that the proposed FTD method can achieve a correction rate of 96.84% for corrupted Huffman files when the source prior information is accurate. More importantly, the proposed method is a general model that can be applied to decompress various types of lossless compressed files of which the original files are natural language texts.     

Adaptive Modulation and Coding with Selective Retransmission under OFDM signaling

In this paper, we present bandwidth efficient selective retransmission method in conjunction with adaptive modulation and coding (AMC) scheme for OFDM waveform. In the proposed method, when a packet failure occurs, receiver requests retransmission of information symbols prone to error corresponding to the low signal-to-noise ratio (SNR) sub-carriers of OFDM modulation. The selective retransmission avoids unnecessary retransmission and AMC chooses a proper modulation and coding scheme with an objective to maximize the throughput. Our method achieves higher throughput as compared to conventional retransmission methods such as Chase combining hybrid automatic repeat reQuest (CC-HARQ) and incremental redundancy hybrid automatic repeat reQuest (IR-HARQ). We also provide the throughput and delay analysis of the proposed method for non-truncated ARQ. The simulation results demonstrate throughput gain without significant impact on delay as compared to the conventional retransmission approaches.