Optimal Matching in Wireless Sensor Networks

In this paper, we study two problems of providing reliable data transmission and developing aggregation techniques for correlated data in wireless sensor networks. A system with forward error correction (FEC) can provide an objective reliability while using less transmission power than a system without FEC. Because of the additional parity bits and encoding/decoding energy consumptions, we study the effect of FEC on energy efficiency. We propose to use LDPC codes for FEC. We show that wireless sensor networks using LDPC codes are almost 45% more energy efficient than those that use BCH codes, which were shown to be 15% more energy efficient than the best performing convolutional codes. Then we study the problem of providing aggregation for two and three correlated nodes in wireless sensor networks. We propose to use LDPC codes in wireless sensor networks for source and channel coding to obtain a two-fold energy savings. For two correlated nodes, we study both distributed source coding and joint source-channel coding. While distributed source coding using LDPC codes was studied before, joint source-channel coding using LDPC codes is introduced for the first time. The difference between our work in distributed source coding using LDPC codes and the previous work lies in the LDPC code design procedure. The simulation results show that our proposed design criteria improves the performance of the source coding. The convergence of the non-uniform LDPC code of our design technique is almost 60% closer to the Slepian-Wolf limit. For three correlated nodes, we study distributed source coding using LDPC codes. We simplified the problem of design procedure to randomly punctured LDPC codes. This is a new approach for designing LDPC codes and the simulation results for code of length 1000 shows that the convergence of the LDPC code is achieved at compression rate 0.3174 which is only 0.08 away from the Slepian-Wolf limit.

Wireless sensor networks (WSNs) are used to collect large amounts of data over a wide area. However, the sensor nodes used in WSNs have a short lifespan because they are generally battery-operated. Research is actively underway to increase the lifespan of WSNs via techniques such as energy harvesting and wireless power transfer (WPT). This study proposes a scheme for clustering and adjusting the WSN data collection rate to alleviate the hotspot problem and improve network connectivity. The proposed scheme is targeted at WSNs in which the sensor nodes harvest solar energy. In addition, an unmanned aerial vehicle (UAV) traverses along a given path and delivers energy to sensor nodes using WPT, while collecting data as a sink node. In this scheme, the number of cluster heads is determined by considering the number of nodes at each hop distance and the maximum amount of data that can be transferred with the available energy. Further, sensor nodes limit the amount of data they collect to reduce the relay load on intermediate nodes. To achieve this, nodes consider the amount of data transferred by parent nodes, especially nodes in the hotspot, and the number of child nodes. The sink node traverses along a given path, and collects the data accumulated in the cluster head while supplying its remaining energy to the cluster head. The amount of energy it transfers is determined by considering the number of cluster heads. Consequently, nodes in the hotspot are prevented from becoming blacked out from a lack of energy, and the energy harvesting efficiency is increased. Simulation results indicate that the proposed scheme reduces the number of blackout nodes in the hotspot areas near cluster heads. Furthermore, the data collection and monitoring performance increases with the increase in network connectivity.

Recent advances in distributed source coding (DSC) for mission-driven wireless sensor networks (WSN) are related to the coding for multiple correlated sensors in applications such as real time target tracking and environment monitoring. The characteristic of these DSC applications provides significant potential opportunities in the associated sensor network to utilize multirate transmissions for enhancing network performance. In this paper, we study a methodology for interplay optimization between routing and DSC in WSN, and propose a novel multirate based routing scheme for mission-driven DSC applications that considerably extends network lifetime. The proposed scheme adopts the rate assignment based on the residual energy, and employs a joint rate and energy scheduling mechanism to meet the end-to-end transmission rate constraint, information precision requirement, and the energy constraints in the network for DSC. Simulation results show that this multirate based routing scheme achieves significantly longer network lifetime compared to other existing research in WSN for DSC applications.

Energy conservation in wireless sensor networks (WSNs) is the primary performance parameter, because of their limited energy resources. Thus, some energy saving technologies should be applied. One of these technologies for WSNs is distributed source coding (DSC), related to correlation between sensor outputs. In this paper, we propose to apply multirate distributed source coding to reduce energy consumption for a target BER value. Low Density Parity Check (LDPC) code is one of the practical codes that can be used for DSC to compress close to Slepian-Wolf limit. The simulation results for codes of length 1000 and three different compression ratios show that the proposed multirate approach achieves better energy efficiency rather than a single rate code with similar maximum BER value.

In this paper, we propose a theoretical setup for evaluation of energy efficiency of wireless sensor networks (WSNs) with distributed source coding (DSC) algorithms and packet aggregation (PA). We consider four topologies for DSC and three alternatives for PA, and the system model includes a realistic network architecture with multi-hop communication, automatic repeat request protocol (ARQ), and packet losses. The analysis is carried out in two steps. Firstly we derive the packet loss probability, and then evaluate the average number of packets transmitted throughout the network. This second performance index can then be mapped onto an energy efficiency indicator. The proposed model is specifically adopted for performance comparison of the different coding strategies and aggregation schemes in terms of energy efficiency. Numerical results show that packet overheads have a relevant influence on performance, while the ARQ protocol introduces negligible effects on the energy consumption. Furthermore, DSC topologies with master- slave approach and fragmentation of packets exhibit better performance.

Error control coding improves reliability and efficiency of wireless communication systems. This research delves into the latest advancements in error control coding schemes for wireless communication systems, with a specific focus on their application within the domain of Low Power Wide Area Networks (LPWANs) such as Narrowband Internet of Things (NB-IoT) systems, LoRa energy-efficient hardware design, random access control systems, and wireless sensor networks. In the context of LPWANs, particularly NB-IoT systems, we investigate the adaptive coding and modulation (ACM) in NB-IoT systems, which adapts coding rates and modulation techniques to changing channel circumstances. Comprehensive analysis of the literature review shows that the proposed approaches are more energy efficient and less error-prone than fixed coding and modulation schemes. Using Cell Design Methodology (CDM), Single Error Correction (SEC) codes are optimized for energy efficiency. Power and energy efficiency have improved in standard CMOS and CDM logic systems. This impacts IoT devices, memory storage, and security systems. Next, Forward Error Correction (FEC) is examined for ALOHA-based random-access control systems wherein packet loss rate and spectral efficiency metrics are mathematically calculated. FEC is beneficial, especially when time and frequency synchronization diverge. Monte Carlo simulations studied in the literature shows that FEC improves satellite communication systems. This review establishes benefits of FEC, notably in satellite communications systems. In addition, Wireless Sensor Networks (WSNs) error control techniques and network reliability and performance are examined along with Convolutional, Turbo, Low density parity check codes(LDPC), and Polar error correction coding (ECC) methods. We learn about their benefits, drawbacks, and implementation challenges by assessing their efficacy, efficiency, and applicability in wireless sensor networks (WSN) applications. This study further highlights advancements of energy-efficient error control coding when, integrated into network designs alongwith the trade-offs between efficacy and intricacy to help improve wireless communication networks. Finally, this study summarizes current error control coding approaches for wireless communication systems. We provide insights into improving reliability, energy conservation, and efficacy by examining ACM, CDM, FEC, and error correction coding (ECC) across varied domains including LPWAN and IoT technologies with earnest hope to facilitate researchers, engineers, and professionals working in the field of error control coding in wireless communication.

Localization is an essential task in Wireless Sensor Networks (WSN) for various use cases such as target tracking and object monitoring. Anchor nodes play a critical role in this task since they can find their location via GPS signals or manual setup mechanisms and help other nodes in the network determine their locations. Therefore, the optimal placement of anchor nodes in a WSN is of particular interest for reducing the energy consumption while yielding better accuracy at finding locations of the nodes. In this paper, we propose a novel approach for finding the optimal number of anchor nodes and an optimal placement strategy of them in a large-scale WSN, based on the output of Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) methods. As an initial step in this approach, the virtual localization process is executed over a virtual coordinate system in order to optimize the efficiency of the localization process. GWO and PSO methods are compared with a coverage-based analytical method and machine learning approaches such as Support Vector Machine (SVM) regression and Multiple Regression. The simulations we run with different numbers of nodes in a WSN and different communication ranges of nodes demonstrate that the proposed approaches are superior for minimizing the localization errors while reducing the number of anchor nodes.

A novel multibeam satellite system design is proposed in this paper based on jointly optimizing power and carrier allocation in order to best match the asymmetric traffic requests. This design introduces higher and asymmetric interference levels throughout the coverage. However, both power and bandwidth will be used more efficiently. Even though the problem of power and bandwidth allocation has been addressed in terrestrial wireless communications, it is new in satellite systems and since architecture and channel are different, available results and algorithms are not applicable to satellite payload systems. In this paper we formulate the resource allocation problem as max-min SINR balancing based on the recently introduced axiomatic-based interference model, but in addition, we also optimize the carrier allocation when performing the SINR balancing problem. An analytical solution for the optimal carrier allocation is proposed and we iteratively find the optimal power allocation for each beam. The Shannon (upper bound) and current state-of-the art PHY layer technology: DVB-S2 are proposed to be implemented in order to obtain the gap between them. Simulation results show significant improvements in terms of power gain, spectral efficiency and traffic matching ratio comparing with conventional system, which is designed based on uniform bandwidth and power allocation.

Distributed source coding has been viewed as an important and challenging technique for the problem of signal recovery in wireless sensor networks, due to limited network bandwidth, processing capability, and power. In this paper, the problem of signal recovery in wireless sensor networks is studied by leverage of side information and distributed source coding. The available bandwidth and packet loss rate are assumed to be changing in the delivery path from remote sensors to sink. We first analyze the impact of quantization, random binning, and transmission on the quality of estimated signal. Then we propose an adaptive control scheme to trade off bandwidth on quantization, random binning, and transmission under specific network scenarios. Numerical results validate the effectiveness of the proposed adaptive control scheme on minimizing system distortion.

Existing work on placing additional relay nodes in wireless sensor networks to improve network connectivity typically assumes homogeneous wireless sensor nodes with an identical transmission radius. In contrast, this paper addresses the problem of deploying relay nodes to provide fault-tolerance with higher network connectivity in heterogeneous wireless sensor networks, where sensor nodes possess different transmission radii. Depending on the level of desired fault-tolerance, such problems can be categorized as: (1) full fault-tolerance relay node placement, which aims to deploy a minimum number of relay nodes to establish k (k ges 1) vertex-disjoint paths between every pair of sensor and/or relay nodes; (2) partial fault-tolerance relay node placement, which aims to deploy a minimum number of relay nodes to establish k (k ges 1) vertex-disjoint paths only between every pair of sensor nodes. Due to the different transmission radii of sensor nodes, these problems are further complicated by the existence of two different kinds of communication paths in heterogeneous wireless sensor networks, namely two-way paths, along which wireless communications exist in both directions; and one-way paths, along which wireless communications exist in only one direction. Assuming that sensor nodes have different transmission radii, while relay nodes use the same transmission radius, this paper comprehensively analyzes the range of problems introduced by the different levels of fault-tolerance (full or partial) coupled with the different types of path (one-way or two-way). Since each of these problems is NP-hard, we develop O(sigmak <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> )-approximation algorithms for both one-way and two-way partial fault-tolerance relay node placement, as well as O(sigmak <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> )-approximation algorithms for both one-way and two-way full fault-tolerance relay node placement (sigma is the best performance ratio of existing approximation algorithms for finding a minimum k-vertex connected spanning graph). To facilitate the applications in higher dimensions, we also extend these algorithms and derive their performance ratios in d-dimensional heterogeneous wireless sensor networks (d ges 3). Finally, heuristic implementations of these algorithms are evaluated via simulations.

In this paper, we have investigated on virtual multiple-input-multiple-output (V-MIMO) in wireless sensor networks (WSNs) and carried out the performance analysis for this system. An energy consumption model is developed for a cooperative virtual MIMO WSN. For a given geographical area and number of nodes, we optimized the values of various performance parameters like, the number of cooperative nodes, number of cluster heads, communication range distance and so on, to ensure increased life time of sensor nodes. Simulation results are presented for different cases and scenarios. It is observed that V-MIMO WSN can be efficiently designed and optimized for the targeted applications.

A simple but powerful scheme for distributed source coding (DSC) based on the concept of binning and syndromes and non traditional turbo codes is proposed. The previous works on the compression with side information using turbo codes and the binning technique are focused on binary turbo codes. The source is considered to be binary or is converted to a binary stream. This conversion, however, reduces the redundancy that could be exploited by the compression algorithm. To achieve higher compression efficiency, the authors propose using a scheme based on a turbo decoder that decides over symbols rather than bits. In the same direction and for further performance improvement, at the cost of increased encoder complexity, they also present a DSC scheme based on non-binary turbo codes. The results demonstrate improved performance. Based on the suggested algorithms, a scheme for gathering real data in wireless sensor networks and assess the corresponding energy savings is proposed.

Purpose – The purpose of this paper is to investigate conditional and unconditional lifetime sequence of wireless sensor networks (WSN) that have many important practical applications. A significant limitation for WSN is its short lifetime due to the limited capacity of the battery. Renewable energy can significantly extend the lifetime of WSN. In this paper, we investigate the whole sequence of lifetimes of every sensor in WSN, as different application scenarios have different requirement on how many sensors can die until the WSN is no longer functional. Design/methodology/approach – Linear programming formulation was used to investigate both the conditional and unconditional lifetime sequence of WSN. The lifetime sequences of WSN without and with differ levels of solar power were studied. Findings – This investigation of lifetime sequences discovered three interesting phenomena: the sensors that die first are on the peripheral of the network, rather close to the base station; multiple sensors tend to die simultaneously; and the lifetimes of sensors that die later can be extended by renewable energy much more significantly than those that die early, which is very good news to applications that can tolerate the death of a fraction of sensors. Originality/value – In this paper, the first optimization formulation for maximizing both unconditional and conditional lifetime sequences of WSNs with renewable energy sources was provided. Only the conditional lifetime sequence has been investigated in a previous work, but this method runs n-times faster than the previous work, with n being the number of nodes in the WSN.

Wireless Sensor Networks being limited energy source, it must function in energy efficient manner to increase the net lifetime of the network i.e. gathering more information for a longer period from the area sensed [1].Because of limited battery charge, bandwidth and nodes in the transmission range direct communication between sensing nodes to the sink is not a optimal solution for both bandwidth and energy point of view. While the LEACH protocol [2] is used as randomizing cluster heads for equal energy dissemination, the PEGASIS protocol [3] uses a chain of cluster heads participating in different rounds for transmitting data to the base station. As the base station can locate at any distances from the individual nodes, in spite of randomization and chain of cluster heads formation, each node actually dissipates a different amount of energy during its turn of transmission to the base station. DHS takes out this energy difference by data handover in specific cycles through suitable node pairing. But the main limitation of this scheme is its inapplicability to networks with large number of nodes due to the huge amount of computations, in LCMs as proposed in [6], to be carried out for determining the cycle in which a particular pair of nodes should execute a handover. This might time as well as energy consuming. Also, the expanse of self-energy in handover depending on the distance between participating nodes will become considerable if too many handovers take place. In this paper, we propose DHS-with-mean, according to which, all the nodes handover in the same mean cycle, thereby eliminating LCMs, and reducing the complexity of the computations remarkably. Also, we have proposed half DHS with mean, in which not all but only 50% of the nodes execute handovers. Finally, we have proposed 10% DHS-we have experimented with the percentage of nodes participating in handover and have found 10% to be the optimal percentage of nodes to be participating in handover for achieving the maximum network lifetime. Extensive simulations show half DHS gives on the average 3.15%, 5.52%, 3.6% and 6.03% improvement in network lifetime over and above the 10%, 12%, 17%, and 20% improvement achieved by DWeMen alone on PEGASIS, the binary model, LEACH and the direct scheme respectively. Moreover, 10% DHS achieves a further 3.15% and 4.88% improvement over half DHS, in case of PEGASIS and binary model respectively. It thus presents a near optimal solution for equal energy dissipation enhancing network lifetime, for any number of nodes

In this paper, we evaluate the end-to-end throughput of CDMA-based Wireless Sensor Networks (WSN) with long-thin topologies using theoretical models. In order to minimize the multiple access interference (MAI) and improve the throughput, a power allocation scheme is required. Instead of using sophisticated power allocation algorithms, which require the knowledge of path losses between all pairs of nodes in the network, a simple heuristic power allocation scheme is proposed and used in this paper. It has been found that in fading environments, our scheme results in better throughput performance and is easy and feasible to be implemented.