Performance Of Wireless Sensor Networks Research Articles

A Novel Hybridization of ML Algorithms for Cluster Head Selection in WSN

Generally, Wireless Sensor Networks (WSNs) are infrastructure-less networks with thousands of sensor nodes that sense or monitor the physical and environmental changes and forward the collected data to a central node. Besides, WSN has become the most efficient technology for handling Internet of Things (IoT) devices. Still, challenges such as node failures, high traffic among the nodes, link failures, etc., limit the performance of WSNs. To solve the challenges in WSN, this paper aims to develop a novel non-uniform clustering model, where the Cluster Heads (CHs) are selected based on the candidate CH selection strategy that transfers the data. Moreover, unbalanced energy utilization and data redundancy are eliminated via multi-hop communication. For attaining the non-uniform clustering model, the routing among the data packets is done by the efficiency of the hybridization of the Machin Learning (ML) algorithms viz Genetic Algorithm (GA) and Lion Algorithm (LA) with the consideration of energy, cost, time, network lifetime, and data accuracy. Finally, the performance of the proposed model is verified and validated through a comparative study with the existing models.

An effective hotspot mitigation system for Wireless Sensor Networks using hybridized prairie dog with Genetic Algorithm.

Wireless Sensor Networks (WSNs) consist of small, multifunctional nodes distributed across various locations to monitor and record parameters. These nodes store data and transmit signals for further processing, forming a crucial topic of study. Monitoring the network's status in WSN applications using clustering systems is essential. Collaboration among sensors from various domains enhances the precision of localised information reporting. However, nodes closer to the data sink consume more energy, leading to hotspot challenges. To address these challenges, this research employs clustering and optimised routing techniques. The aggregation of information involves creating clusters, further divided into sub-clusters. Each cluster includes a Cluster Head (CH) or Sensor Nodes (SN) without a CH. Clustering inherently optimises CHs' capabilities, enhances network activity, and establishes a systematic network topology. This model accommodates both multi-hop and single-hop systems. This research focuses on selecting CHs using a Genetic Algorithm (GA), considering various factors. While GA possesses strong exploration capabilities, it requires effective management. This research uses Prairie Dog Optimization (PDO) to overcome this challenge. The proposed Hotspot Mitigated Prairie with Genetic Algorithm (HM-PGA) significantly improves WSN performance, particularly in hotspot avoidance. With HM-PGA, it achieves a network lifetime of 20913 milliseconds and 310 joules of remaining energy. Comparative analysis with existing techniques demonstrates the superiority of the proposed approach.

Reducing Energy Consumption and Clustering in Wireless Sensor Networks using an Improved Discrete Gorilla Troops Optimization Algorithm with Fuzzy Rules

Among the most common and extensively operated methods in wireless sensor networks (WSN) to increase the efficiency and performance of WSN is network clustering because, through this solution, data is transmitted through the closest possible path. In the clustering method, a portion of the nodes in the network become cluster heads, then a cluster is formed by joining the nodes close to the cluster head. However, the main and important problem in this issue is preventing the creation of unbalanced clusters since the spreading of cluster heads in the network can be unequal. In this paper, we have presented an algorithm based on the discrete Gorilla Troops Optimizer (DGTOA) algorithm and K-Means with a fuzzy clustering approach. In this model, first, several cluster heads are chosen by applying the discrete DGTOA algorithm, and then the output of the DGTOA algorithm is given as initial points and coordinates in K-means. Finally, the cluster head is utilized to wield the fuzzy system. Also, different criteria and graphs were used to compare the proposed model, and the obtained results were measured with other methods, and the obtained outcomes indicate the high performance of the proposed model.

A Novel Scheduling Algorithm for Improved Performance of Multi-Objective Safety-Critical WSN Using Spatial Self-Organizing Feature Map

Technological advances in the internet of things (IoT) allowed a low-cost, yet small sensor device to operate with limited power in a dynamic harsh environment where human intervention is impossible. The wireless sensor network (WSN) is an example of the IoT in which physical devices’ software and sensors can interconnect to provide application services. It is important that such applications be dependable to meet the required quality of service (QoS) and function as expected. Consequently, the multi-objective optimization (MOO) problem in WSNs aims to address the trade-off among coverage, connectivity, and network lifetime requirements. Node scheduling is one approach of many used to optimize energy in WSNs. The contribution of this work is the proposal of a self-organizing feature map (SOFM) to enhance the node scheduling in WSNs. The proposed SOFM node-scheduling algorithm aims to spatially explore the state space domain and obtain an optimal solution. In our experiment, the proposed SOFM node-scheduling algorithm is evaluated against a comparable algorithm, namely the BAT node-scheduling algorithm, via MATLAB simulator. The results showed that the SOFM node-scheduling algorithm outperformed the latter by 27% and 28% for the maximum and minimum coverage, respectively, with similar performance of 99% of connectivity and network lifetime.

Studies on energy efficient techniques for agricultural monitoring by wireless sensor networks

Wireless Sensor Networks (WSN) are pivotal for real-time monitoring in diverse sectors through the Internet of Things (IoT). However, current wireless communication protocols face challenges in integrating Precision Agriculture (PA) management systems with Artificial Intelligence (AI). PA optimizes nutrient supply and pesticide use, enhancing crop yield and product quality. Despite these advantages, structural changes in WSNs, including deployment, coverage, scalability, and energy consumption, hinder their potential. This paper conducts a comparative study of WSNs in agriculture, focusing on wireless protocols, energy harvesting, and efficiency procedures. The research delves into challenges faced by WSNs in agriculture, emphasizing the need for power-saving agricultural management strategies to ensure sustained, effective long-term monitoring. The study addresses integration issues, explores agricultural benefits, and proposes solutions to enhance WSN performance in dynamic field environments.

Mathematically Engineered Adams Optimizer for Energy Efficient and Optimal Routing Approach for the Wireless Sensor Network

Wireless Sensor Network (WSN) comprises numerous Sensor Nodes (SN) scattered across a network to observe the atmospheric condition where the SN is the minimum cost. Vast usage of energy during data transmission is the primary design issue in WSNs that can be addressed by routing and clustering techniques. WSN has diverse transmission paths with unstable nodes, where the energy consumption is high, and the Quality of Service (QoS) is affected. The high data transmission delay and inefficient throughput indicate the ineffectiveness of WSN. To overcome this issue, this research concentrates on energy-efficient optimal routing formulated with the assistance of a mathematical approach and Adams optimizer. The mathematics’ based Pareto optimization is utilized to optimize the Adam Moment Estimation (Adam) that trains Deep Learning (DL) network that is deployed in both heterogeneous and homogeneous networks. The learning process is enhanced with the support of Pareto optimization, and the multi-objective problem is efficiently handled. In this context, Pareto optimization balances the path construction, and the equitable distribution issue is rectified by the Adams-based DL network. The proposed Pareto-integrated Adams Optimizer for Energy Efficient Routing (PAOEER) sustains the WSN performance by enhancing network parameters. The PAOEER achieves a higher Packet Delivery Ratio (PDR) of 97.18% and minimal Energy Consumption (EC) of 112.34 J. The simulation analysis shows that the proposed PAOEER is effective and outperforms the existing state-of-the-art techniques, indicating PAOEER is a promising alternative.

Cycle‐Consistent Generative Adversarial Network and Crypto Hash Signature Token‐based Block chain Technology for Data Aggregation with Secured Routing in Wireless Sensor Networks

SummaryA fundamental component of Wireless Sensor Networks (WSN) is routing, because it is responsible for data transmission to the base stations (BS). Routing attacks are attacks that have the ability to interfere with the operation of WSN. A reliable routing system is needed for guaranteeing routing security including WSN effectiveness. Many studies have been conducted to improve trust among routing nodes, cryptographic algorithms and centralized routing decisions. Nevertheless, most of the routing techniques are not practical because it is challenging to detect suspicious actions of routing nodes. Generally, there is lack of reliable technique for preventing malicious node attacks. Therefore in this manuscript, Cycle‐Consistent Generative Adversarial Network (CCGAN) optimized with Ebola Optimization Search Algorithm (EOSA) and Crypto Hash Signature (CHS) Token‐based Block chain (BC) Technology for Data Aggregation with Secured Routing in Wireless Sensor Networks (SR‐CCGAN‐EOSA‐BDA‐WSN) is proposed for data aggregation with secured optimum routing in WSN. The proposed methodology uses a Proof of Authority (PoA) method in the block chain network to authenticate the process of node transmission. A Cycle‐Consistent Generative Adversarial Network optimized with the Ebola optimization algorithm methodology (CCGAN‐EOSA) is used to select the validation group required for proofing and select the proper next hop as a forwarding node proficient of securely and easily transmitting messages. The performance metrics, like delay, average latency with energy consumption, block chain token transactions throughput is analyzed. The performance of SR‐CCGAN‐EOSA‐BDAWSN proposed method provides 76.26%, 65.57%, and 42.9% lesser delay under 30% spiteful routing environment; 73.06% are compared with existing methods.

WiCHORD+: A Scalable, Sustainable, and P2P Chord-Based Ecosystem for Smart Agriculture Applications

In the evolving landscape of Industry 4.0, the convergence of peer-to-peer (P2P) systems, LoRa-enabled wireless sensor networks (WSNs), and distributed hash tables (DHTs) represents a major advancement that enhances sustainability in the modern agriculture framework and its applications. In this study, we propose a P2P Chord-based ecosystem for sustainable and smart agriculture applications, inspired by the inner workings of the Chord protocol. The node-centric approach of WiCHORD+ is a standout feature, streamlining operations in WSNs and leading to more energy-efficient and straightforward system interactions. Instead of traditional key-centric methods, WiCHORD+ is a node-centric protocol that is compatible with the inherent characteristics of WSNs. This unique design integrates seamlessly with distributed hash tables (DHTs), providing an efficient mechanism to locate nodes and ensure robust data retrieval while reducing energy consumption. Additionally, by utilizing the MAC address of each node in data routing, WiCHORD+ offers a more direct and efficient data lookup mechanism, essential for the timely and energy-efficient operation of WSNs. While the increasing dependence of smart agriculture on cloud computing environments for data storage and machine learning techniques for real-time prediction and analytics continues, frameworks like the proposed WiCHORD+ appear promising for future IoT applications due to their compatibility with modern devices and peripherals. Ultimately, the proposed approach aims to effectively incorporate LoRa, WSNs, DHTs, cloud computing, and machine learning, by providing practical solutions to the ongoing challenges in the current smart agriculture landscape and IoT applications.

Location Based Power Reduction Cloud Integrated Social Sensor Network

It is great to hear about the advancements in wireless sensor networks and their applications, as well as the integration of cloud computing to enhance data analysis and storage capabilities. Indeed, these technologies have opened up numerous possibilities across various fields, including infrastructure tracking, environmental monitoring, healthcare, and more. The concept of a social sensor cloud, as you mentioned, brings an interesting dimension to this technology landscape by focusing on knowledge-sharing and connecting like-minded individuals or organizations. This could potentially lead to more collaborative and efficient solutions across a wide range of domains. Energy efficiency is a critical consideration in the design and operation of wireless sensor networks and the cloud infrastructure that supports them. The limited battery life of sensors necessitates careful management of energy consumption to ensure optimal functionality and longevity. Sleep scheduling methods are a common technique used to manage energy consumption in these networks. By coordinating when sensors are active and when they are in a low-power sleep mode, energy consumption can be significantly reduced without compromising the network's overall effectiveness. In the context of the Social Sensor Cloud, managing energy efficiency becomes even more crucial due to the shorter battery life of the sensors involved. This is particularly relevant given the growing concerns about environmental sustainability and the need to reduce energy consumption across technological systems. It's clear that your research paper addresses these challenges head-on, by exploring energy-efficient techniques for the Social Sensor Cloud. Sleep scheduling is just one of the many strategies that researchers and engineers are working on to strike a balance between functionality and energy consumption. Other methods might include optimizing data transfer protocols, developing energy-harvesting mechanisms, and enhancing sensor hardware efficiency. As technology continues to evolve, the integration of wireless sensor networks, cloud computing, and social networks will likely pave the way for innovative solutions and transformative applications. Addressing energy efficiency concerns will undoubtedly play a crucial role in ensuring the long-term viability and positive impact of these technologies.

Enhancing the Performance of 6LoWPAN-Based Wireless Sensor Networks (WSNs) with IEEE 802.11AH (Wi-Fi HaLow)

6LoWPAN allows IEEE 802.15.4 standard-based wireless sensor networks (WSNs) to be connected to the Internet through the Internet protocol IPv6; however, its performance decreases as the network grows in size due to complications such as the bottleneck problem; therefore this paper aims to improve the performance of 6LoWPAN-based WSNs by using the IEEE 802.11AH standard as a backbone in these networks, since this emerging new technology is suitable for IoT applications, as it can provide a coverage range up to 1,000 m and data transmission rates up to 78 Mbps. The IEEE 802.11AH standard can be used as a backbone by utilizing its characteristics to improve some of the performance parameters of the 6LoWPAN networks such as end-to-end delay, frames delivery ratio, and the network throughput. This paper proposes a heterogeneous network infrastructure consisting of 6LoWPAN and the IEEE 802.11AH standard to enable bidirectional data transmission between the network components and the Internet via the Internet protocol IPv6 with two types of gateways, the edge gateways to connect the entire network to the Internet and the intermediate gateways to connect the 6LoWPAN clusters to the edge gateways. The simulation results demonstrate that the heterogeneous network approach can provide significant improvement gains compared to the 6LoWPAN homogeneous networks, since it shows clear improvements to the packets delivery ratio, the end-to-end delay mean value and the network throughput, subsequently leading to a distinct enhancement in the network’s overall reliability.

An Optimized Reinforcement Learning-Based Routing Protocol Using Spider Monkey Optimization Algorithm

In Wireless Sensor Networks, numerous machine-learning algorithms are used in the packet routing strategy. The recommended routing approach is implemented using adaptive reinforcement learning and a spider monkey optimization algorithm. Data packets are sent over the network via optimum route establishment—the above attempts to save energy by changing the Q-value at each instance. Technology enables adaptive mechanisms to demonstrate intelligent behavior in complex and dynamic situations such as Wireless Sensor Networks (WSN). Recently, new routing methods for WSNs have been devised in response to such a notion. Due to user interference and overall network performance, the numerous routing pathways cause frequent channel-switching occurrences. The spider monkey optimization method obtains routing metrics such as bandwidth, end-to-end latency, throughput, overhead, and packet delivery ratio. Compared to other machine learning algorithms, such as RLBR, the performance of WSN is improved and obtained better performance results.

Energy-efficient mobile sink data collection in wireless sensor networks using LEACH with rendezvous nodes

In Wireless Sensor Networks (WSNs), energy consumption management poses a formidable challenge due to the energy-intensive nature of data transmission and processing. WSNs consist of densely deployed sensor nodes, and data collection is carried out by cluster heads. To address the issue of transmitting data to the mobile sink (MS) and extend the network's lifetime, rendezvous nodes(RNs) are utilized. However, existing approaches have not sufficiently improved network longevity. To tackle this problem, a novel protocol called Energy Efficient Mobile Sink Data Collection (EEMSDC) is proposed. This protocol aims to balance energy usage, prevent sensor node overload, and facilitate data delivery to the MS through RNs. The proposed approach optimizes the MS's tour in WSNs by employing predetermined TDMA slots for efficient data transmission with RNs. The proposed protocol outperforms existing protocols such as LEACH, EAMMH, LEACH-C, MOD-LEACH, MGEAR, and TDEEC in terms of network lifetime, energy consumption, and data loss. By reducing network overhead and ensuring balanced energy consumption among sensor nodes, the proposed approach significantly extends the network's lifetime. The effectiveness of the proposed protocol is validated through mathematical analysis and simulations conducted using MATLAB 2021a. The simulations show a remarkable reduction in energy consumption by up to 68% and an impressive increase in network lifetime by 73% compared to LEACH. These findings underscore the substantial advantages of the proposed approach in enhancing the performance of WSNs.

Impacts of Diversity Technique Application in Order to Reduce the Fading in IEEE 802.15.4 Networks

The paper discusses the operation of wireless sensor networks consisting of nodes based on the IEEE 802.15.4 ZigBee transceiver operating in the presence of Rayleigh or Nakagami-m fading, additive white Gaussian noise (AWGN) and interference. Analytical expressions for bit error rate (BER) for the IEEE 802.15.4 network were obtained when Rayleigh and Nakagami-m fading were processed with a selection-combining (SC) and maximum-ratio-combining (MRC) combiner and when AWGN and interference were present. Analytical results are presented graphically in the form of BER change, depending on the signal-to-noise ratio (SNR) for different values of network parameters: fading parameter (m), number of branches of MRC combiner (L), modulation parameter (M), and spreading sequence lengths (Lc). Graphical results are presented for the system when there are no combiners and when the SC and MRC combiners are included. Numerical results were confirmed by Monte Carlo simulation. Analysis of the SC and MRC combiner application impact on the performance improvement of the IEEE 802.15.4 network and the received signal was performed.

Region-Different Network Reconfiguration in Disjoint Wireless Sensor Networks for Smart Agriculture Monitoring

Connectivity restoration is essential for ensuring continuous operation in wireless sensor networks (WSNs). However, existing works lack enough network robustness when suffering from the secondary external damages. In this paper, we propose a novel connectivity restoration scheme to address this problem. This scheme comprises three connectivity mechanisms regarding relay segment selection in different regions. The first one is a data traffic decentralization mechanism, which establishes more transmission paths near the sink for reliability improvement and traffic load balancing. The second one is a segment shape selection mechanism, in which the segments with high-reliability preferably become the relay segments for greater network robustness. The third one is a traffic load transfer mechanism, in which data traffic is transferred from a high-load segment to a low-load segment for balancing energy depletion of the network. The distinctive characteristics of this work are twofold: different regions perform diverse connectivity restoration approaches according to the demand diversity of different regions, and traffic load can be balanced from upstream regions rather than only from downstream regions. Extensive simulation experiments validate the effectiveness and advantages of our proposed scheme in terms of connection cost, network robustness, load balance degree, and network longevity.

Virtual Grid-Based Routing for Query-Driven Wireless Sensor Networks

In the context of query-driven wireless sensor networks (WSNs), a unique scenario arises where sensor nodes are solicited by a base station, also known as a sink, based on specific areas of interest (AoIs). Upon receiving a query, designated sensor nodes are tasked with transmitting their data to the sink. However, the routing of these queries from the sink to the sensor nodes becomes intricate when the sink is mobile. The sink’s movement after issuing a query can potentially disrupt the performance of data delivery. To address these challenges, we have proposed an innovative approach called Query-driven Virtual Grid-based Routing Protocol (VGRQ), aiming to enhance energy efficiency and reduce data delivery delays. In VGRQ, we construct a grid consisting of square-shaped virtual cells, with the number of cells matching the count of sensor nodes. Each cell designates a specific node as the cell header (CH), and these CHs establish connections with each other to form a chain-like structure. This chain serves two primary purposes: sharing the mobile sink’s location information and facilitating the transmission of queries to the AoI as well as data to the sink. By employing the VGRQ approach, we seek to optimize the performance of query-driven WSNs. It enhances energy utilization and reduces data delivery delays. Additionally, VGRQ results in ≈10% and ≈27% improvement in energy consumption when compared with QRRP and QDVGDD, respectively.

A Hybrid Firefly with Dynamic Multi-swarm Particle Swarm Optimization for WSN Deployment

<p>Enhancing the coverage area of the sensing range with the limiting resource is a critical problem in the wireless sensor network (WSN). Mobile sensors are patched coverage holes and they also have limited energy to move in large distances. Several recent studies indicated the metaheuristic algorithms can find an acceptable deployed solution in a reasonable time, especially the PSO-based algorithm. However, the speeds of convergence of most PSO-based algorithms are too fast which will lead to the premature problem to degrade the quality of deployed performance in WSN. A hybrid metaheuristic combined with dynamic multi-swarm particle swarm optimization and firefly algorithm will be presented in this paper to find an acceptable deployed solution with the maximum coverage rate and minimum energy consumption via static and mobile sensors. Moreover, a novel switch search mechanism between sub-swarms will also be presented for the proposed algorithm to avoid fall into local optimal in early convergence process. The simulation results show that the proposed method can obtain better solutions than other PSO-based deployment algorithms compared in this paper in terms of coverage rate and energy consumption.</p> <p> </p>

A novel cluster head selection algorithm based IoT enabled heterogeneous WSNs distributed architecture for smart city

The Internet of Things (IoT) is a revolutionary development that facilitates the instant communication of data among devices, making it an essential paradigm in today's world. It enables the development of smart infrastructure and provides assistance in emergency services during disasters. However, there is a growing need to develop energy-conserving paradigms that cover all aspects of a smart city. In this work, the authors present a hybrid genetic algorithm (GA) inspired greedy strategy-based mutation operation for IoT-enabled heterogeneous wireless sensor networks (WSNs) distributed architecture in a technology driven city. The proposed protocol aims to design a weighted fitness function that considers four parameters: node density, residual and average energy, and distance parameters. Additionally, a 3-tier heterogeneity is considered to prolong the network functional duration, and a deployment strategy is addressed to position these nodes in an energy-efficient manner. The proposed algorithm achieves 31.41%, 20.42%, 13.37%, 1.59% increase in network lifespan compared to GA-based optimized clustering (GAOC), optimized GA with single sink (OptiGAS-StSS), multiple sinks (MS) based GAOC and optimized GA with multiple sink (OptiGAS-StMS), respectively. The significant enhancement in network lifespan is an outcome of choosing the most efficient CH nodes based on the proposed fitness function. Overall, the proposed protocol contributes to developing an energy-conserving paradigm for a smart city.

Enhancing the lifetime and performance of WSNs in smart irrigation systems using cluster‐based selection protocols

AbstractThis paper presents a comparative analysis of cluster‐based selection protocols for wireless sensor networks (WSNs) in smart irrigation systems. The study evaluates the performance of stable election protocol (SEP)‐based protocols, including conventional SEP, zonal SEP (ZSEP), enhanced SEP (ESEP) and threshold‐sensitive SEP (TSEP), and distributed energy‐efficient clustering (DEEC)‐based protocols, including conventional DEEC, developed DEEC (DDEEC), enhanced DEEC (EDEEC) and threshold‐sensitive DEEC (TDEEC). Simulation results show that TSEP and ZSEP outperform other SEP‐based protocols in terms of network lifetime, stability period, number of dead nodes and throughput. EDEEC and TDEEC show superior performance compared to other DEEC‐based protocols. However, TDEEC and EDEEC have a lower performance in terms of packets sent to cluster heads (CHs) due to the three levels of energy heterogeneity, which reduce the average packets sent to CHs. Finally, a comparative analysis between the SEP‐based and DEEC‐based protocols is presented. The findings reveal that TSEP outperforms all other protocols in terms of the performance metrics examined in this paper, except for throughput, which is higher in the case of ZSEP. Overall, the proposed cluster‐based selection protocols improve the energy efficiency and lifetime of WSNs in smart irrigation systems, and the results demonstrate their effectiveness in supporting the performance of smart irrigation systems.

Exploring the tradeoff between energy dissipation, delay, and the number of backbones for broadcasting in wireless sensor networks through goal programming

Broadcasting, which is an essential mode of operation in wireless sensor networks (WSNs), dissipates a non-negligible portion of the energy budget of a sensor node. Broadcasting is achieved by the dissemination of broadcast packets (originated at the BS) by a set of relay nodes, which constitute a backbone so that all sensor nodes receive broadcast packets. Utilization of multiple backbones is necessary to achieve balanced energy dissipation of sensor nodes in broadcasting. In this study, we propose two mixed integer programming (MIP) models (i.e., the flow-based model and the node-based model), which minimize the energy dissipation of the highest energy-consuming node for broadcasting by utilizing multiple backbones. Balanced energy minimization and delay minimization objectives are integrated through a goal programming (GP) framework built upon the foundation provided by the scalable node-based model. Performance evaluations based on the optimal solutions of our models reveal that maximum energy dissipation and delay in WSN broadcasting can be significantly reduced, simultaneously, by utilizing multiple backbones (e.g., with two backbones maximum energy dissipation and delay can be, concurrently, reduced by more than 2% and 22%, respectively, in comparison to the single backbone case, likewise, it is also possible to simultaneously reduce maximum energy dissipation and delay more than 8% and 13%, respectively, depending on the priorities assigned to the objectives). Nevertheless, employing more than two backbones does not provide any significant performance improvements.

A High Precision Joint Time Synchronization and Location Algorithm for Moving Target

Time synchronization is the guarantee of normal operation of wireless sensor networks, and it is the premise of ranging and location. At present, most researches focus on joint time synchronization and location of non-moving targets, while there are few types of research on joint time synchronization and location of moving targets. Therefore in this paper, under the condition that the time synchronization of the moving target is not carried out, and the information measurement means such as the speed and position of the moving target are not available. We established the system model of joint time synchronization and location with the help of the anchor nodes whose clock has been synchronized and location is known, and derived the Cramer-Rao lower bound (CRLB) of the system. Then we proposed the two-step least square method and iterative method. The former has lower computation and suboptimal performance, while the latter has higher time synchronization accuracy and positioning accuracy, which can attain CRLB. The results are verified by simulation.