Performance Of Wireless Sensor Networks Research Articles

Reducing Delay and Maximizing Lifetime for Wireless Sensor Networks With Dynamic Traffic Patterns

Fast and energy efficient data collection is the most important function of wireless sensor networks (WSNs) which governs the performance of WSNs. Time division multiple access (TDMA) is an effective data collection strategy in which node only schedules the active slot to save energy when transmitting data. However, many previous studies are designed for static traffic pattern such that each node to generate one data packet in a data collection cycle. Moreover, all nodes in the network use the same transmission radius r, so that the network data collection has a larger delay, a lower lifetime, and energy utilization rate. In view of the shortcomings of previous research, this paper proposes a reducing delay and maximizing lifetime (RDML) data collection strategy for WSNs with dynamic traffic patterns. The main innovation point of our work is the data transmission radius is no longer unified as r, while some nodes with energy surplus use a data transmission radius of mr(m > 1). The following network performance can be achieved by adopting the RDML scheme. First, because the nodes in the near sink mr radius area can send data directly to sink, the amount of data carried by hotspots nodes in the original strategy is greatly reduced, and the lifetime is improved. Second, since some nodes can go mr distance to the sink in data transmission, thus accelerating the data routing and reducing the time needed for data collection. Finally, since the remaining energy of nodes is fully utilized to increase the radius of data transmission, the energy utilization rate can be effectively improved. Through the theoretical analysis and experimental results, the RDML scheme can reduce the maximum delay of data collection by 22.32% and improve lifetime as well as the energy utilization rate by 0.86%-84.40%, and 23.33%-88.00%, respectively, compared with previous strategies.

Energy-Efficient Mobile-Sink Sojourn Location Optimization Scheme for Consumer Home Networks

With the fast development of wireless communication and semiconductor devices, home networking has gained considerable attention in the past few years. In Wireless Sensor Networks (WSNs), Sensing coverage is known as one of the most important standards in evaluating the performance of WSNs. The sensor nodes may have failures due to energy depletion during their service life which leads to incomplete coverage of the sensing area and coverage holes in the network which results in reducing the network performance. Therefore, designing an energy-efficient solution to prevent the coverage holes is vital. Employing Mobile Sink (MS) is a popular technique to enhance the energy efficiency of the network. Furthermore, sojourn location optimization is one of the main design issues of mobility based schemes. However, to the best of our knowledge, most of existing sojourn location optimization-based schemes are specially designed for homogeneous networks and much less attention has been devoted to optimizing this parameter in heterogeneous networks. Therefore, the aim behind this paper is to optimize the sojourn location of MS in a heterogeneous home network, which results in improving the coverage time and the performance of the network. Based on the experimental results, the scheme proposed in this paper remarkably improves the balanced energy consumption, which leads to enhanced network lifetime and performance.

Intruder Attacks on Wireless Sensor Networks: A Soft Decision and Prevention Mechanism

Because of the wide-ranging of applications in a variety of fields, such as medicine, environmental studies, robotics, warfare and security, and so forth, the research on wireless sensor networks (WSNs) has attracted much attention recently. WSNs offer economical, flexible, scalable and pragmatic solutions in many situations. Sensor nodes are tiny and have a limited, non-rechargeable battery source, small memory/computational abilities and low transmitter power. Energy resources are vital as once the battery is depleted, the node is no longer usable. Multiple medium access control (MAC) protocols are designed to increase the life cycle of a node by minimizing its unnecessary energy consumption. In some critical applications like the surveillance of enemy movements on a battlefield, opponents deploy adversary nodes to disturb the performance of WSNs by mainly depleting the battery sources of legitimate nodes. In this work, an intrusion detection mechanism has been adapted to detect different kinds of intruders’ attacks in MAC protocols of WSN’s. A soft decision mechanism has been implemented to detect collision and exhaus-tion attacks. A preventative mechanism has also been introduced, which helps a node to avoid these intrusive attacks. Results show how the lifetime of a node increases and network performance also increases with better throughput and reduced delay.

Deep Learning Based Proactive Caching for Effective WSN‐Enabled Vision Applications

Wireless Sensor Networks (WSNs) have a wide range of applications scenarios in computer vision, from pedestrian detection to robotic visual navigation. In response to the growing visual data services in WSNs, we propose a proactive caching strategy based on Stacked Sparse Autoencoder (SSAE) to predict content popularity (PCDS2AW). Firstly, based on Software Defined Network (SDN) and Network Function Virtualization (NFV) technologies, a distributed deep learning network SSAE is constructed in the sink nodes and control nodes of the WSN network. Then, the SSAE network structure parameters and network model parameters are optimized through training. The proactive cache strategy implementation procedure is divided into four steps. (1) The SDN controller is responsible for dynamically collecting user request data package information in the WSNs network. (2) The SSAEs predicts the packet popularity based on the SDN controller obtaining user request data. (3) The SDN controller generates a corresponding proactive cache strategy according to the popularity prediction result. (4) Implement the proactive caching strategy at the WSNs cache node. In the simulation, we compare the influence of spatiotemporal data on the SSAE network structure. Compared with the classic caching strategy Hash + LRU, Betw + LRU, and classic prediction algorithms SVM and BPNN, the proposed PCDS2AW proactive caching strategy can significantly improve WSN performance.

Genetic Algorithm for Effective Optimization of Delay Performance in Wireless Sensor Networks

Wireless sensor networks (WSNs) play an important role in many real-world applications like surveillance. Wireless networks are also used to have data transfer. In such cases, there are problems with resourcece-constraintnednetworks. The problems include a delay in communication and reduction in Quality of Service (QoS). Topology control can solve this problem to some extent. However, the delay performance and QoS need to be improved further to support intended operations in wireless networks. When relay node concept is considered, it is possible to optimize performance in such networks. In this paper, we proposed a Genetic Algorithm (GA) based relay configuration for optimizing delay performance in WSN. Relay nodes compute optimal positions using the proposed algorithm so as to improve QoS and reduce delay as much as possible. We implemented the algorithm using NS2 simulations. The results revealed that the proposed approach is able to improve QoS, reduce delay besides improving network performance in terms of throughput, network capacity, and energy efficiency.

An Energy-Efficient Fail Recovery Routing in TDMA MAC Protocol-Based Wireless Sensor Network

Conventional IoT applications rely on seamless data collection from the distributed sensor nodes of Wireless Sensor Networks (WSNs). The energy supplied to the sensor node is limited and it depletes after each cycle of data collection. Therefore, data flow from the network to the base station may cease at any time due to the nodes with a dead battery. A replacement of the battery in WSNs is often challenging and requires additional efforts. To ensure the robust operation of WSNs, many fault recovery routing mechanisms have been proposed. Most of the previous fault recovery routing methods incur considerable delays in recovery and high overhead in either energy consumption or device cost. We propose an energy-efficient fail recovery routing method that is aimed to operate over a data aggregation network topology using a TDMA media access control (MAC). This paper introduces a novel fault recovery routing algorithm for TDMA-based WSNs. It finds an optimal neighbor backup parent (NBP) for each node in a way that reduces the energy consumption. The proposed method allows the NBPs to utilize the time slot of the faulty parent nodes, so it eliminates the overhead of TDMA rescheduling for NBPs. To evaluate the fault recovery performance and energy efficiency of the proposed method, we implemented it in C++ simulation program. Simulation experiments with an extensive set of network examples demonstrate that the proposed method can extend the network lifetime by 21% and reduce the energy consumption by 23% compared with the reference methods.

Multi-level clustering protocol for load-balanced and scalable clustering in large-scale wireless sensor networks

The advent of wireless sensor networks (WSNs) has revolutionized the field of smart applications. In order to improve the performance of WSNs, refinement of clustering and routing protocols can make a vast difference. Existing classical and evolutionary optimization technique-based protocols have high computational complexity since clustering and routing problems are solved separately. Moreover, these protocols suffer from hot-spot problem due to uneven formation of clusters. In this paper, we propose a multi-level clustering protocol (MLCP) for energy-efficient data gathering in large-scale WSNs. Additionally, a hierarchical clustering architecture is designed in MLCP to jointly solve the problems of clustering and routing. Further, for the purpose of cluster head selection, a hybrid dragonfly algorithm-based particle swarm optimization technique is proposed which combines the exploration and exploitation capabilities of dragonfly algorithm and particle swarm optimization, respectively. MLCP considers intra-cluster distance, node degree and inter-cluster distance for the formation of scalable, load-balanced and energy-efficient clusters. To demonstrate the full potential of MLCP, network simulations have been carried out in diverse network conditions. MLCP has shown up to 90% increase in the network lifetime and an improvement of 19.36% in conservation of energy in comparison with the competent protocols. The comparison of obtained results with state-of-the-art clustering protocols clearly establishes the superiority of MLCP in achieving load-balanced, scalable and energy-efficient clustering.

An analysis of energy efficiency in Wireless Sensor Networks (WSNs) applied in smart agriculture

In this paper the usage of Wireless Sensor Networks (WSN) in smart agriculture applications was analyzed. The main focus of the paper is on the power consumption of the various WSN components, on the both levels, physical and functional. The analysis, from the energy efficiency aspect, includes a comparative review and discussion of the most commonly used protocols on the physical, data link and network layers. The analysis outcome provides a precise identification of the main power consumers, the magnitude of their consumption, and a deep understanding of the key mechanisms that should be applied in order to improve the energy efficiency in a WSN. The analysis also includes simulation of a WSN operation in a smart agriculture application. The simulation scenario and the measured values of the average power consumption and the average time of activity of the radio component of each network node provide a confirmation of the key points of the previously performed analysis and detailed insights into the possible directions of the strategy for energy efficiency improvement. Additionally, the simulation results reveal the magnitude of the energy savings that can be accomplished by deploying the duty cycle mechanisms within the WSNs. Finally, the paper includes a discussion about various factors and the way they impact the level of energy efficiency, which have to be addressed within the requirements gathering, comprehensive analysis and the design phases of a WSN life cycle implementation.

A Taxonomy for MAC Protocols in Wireless Sensor Networks Based on Traffic Prioritization

Various media access control (MAC) protocols have been developed to deal with the heterogenous traffic in wireless sensor networks (WSN). In many emerging applications, it is regarded essential to prioritize the heterogeneous traffic for reliable performance of WSN. This work categorizes the recently developed traffic prioritization MAC protocols in the classes of ‘Adaptive Contention’, ‘Duty-Cycle Adaptation’, ‘Queue Management’ and ‘Hybrid’. A taxonomy has been developed based on the categorization and detailed overview of each protocol is presented. The performance benefits and gaps are described for each of the protocols and the future research directions are suggested based on the analysis. It has been identified that most of the existing MAC protocols, although provide early channel access to high priority traffic, fail to offer true preemptive priority. There exists no mechanism which could pause/interrupt the ongoing transmission of the low priority traffic to facilitate the immediate channel access for high priority data. The analysis provided by this article is expected to aid the future researchers in identifying the trends of MAC protocol development in the context of traffic prioritization.

A Two-Tier Distributed Fuzzy Logic Based Protocol for Efficient Data Aggregation in Multihop Wireless Sensor Networks

This study proposes a two-tier distributed fuzzy logic based protocol (TTDFP) to improve the efficiency of data aggregation operations in multihop wireless sensor networks (WSNs). Clustering is utilized for efficient aggregation requirements in terms of consumed energy. In a clustered network, member (leaf) nodes transmit obtained data to cluster-heads (CHs) and CHs relay received packets to the base station. In multihop wireless networks, this CH-generated transmission occurs over other CHs. Due to the adoption of a multihop topology, hotspots and/or energy-hole problems may arise. This article proposes a TTDFP to extend the lifespan of multihop WSNs by taking the efficiency of clustering and routing phases jointly into account. TTDFP is a distribution-adaptive protocol that runs and scales sensor network applications efficiently. Additionally, along with the two-tier fuzzy logic based protocol, we utilize an optimization framework to tune the parameters used in the fuzzy clustering tier in order to optimize the performance of a given WSN. This paper also includes performance comparisons and experimental evaluations with the selected state-of-the-art algorithms. The experimental results reveal that TTDFP performs better than any other protocols under the same network setup considering metrics used for comparing energy-efficiency and network lifespan of the protocols.

COO-MAC: A Novel Cooperative MAC Protocol for Wireless Sensor Networks

To improve transmission performance of wireless sensor networks (WSN), we design a media access control layer protocol in this article. A novel channel allocation scheme for nodes is proposed in the protocol based on maximization channel gain. Detail steps of the proposed protocol and nodes access assignment based on channel gains are described. To reduce transmission collision and solve disconnection problem, a retransmission scheme is presented according to carrier sensing multiple access with collision avoid scheme. Furthermore, we extend the protocol application from single transmission environment networks to different transmission environment networks. The numerical analysis and simulation results clearly show that the proposed protocol has a better transmission probability both analytically and via simulation in improving accessing fairness. These results also show that the proposed protocol can improve the performance of WSN efficiently on throughput and transmission fairness.

A trust enhancement scheme for cluster-based wireless sensor networks

Security is crucial in wireless sensor networks (WSNs) since a growing variety of applications require strict security. Cluster-based architecture has an enhancement in the system performance of WSNs. This article presents a trust enhancement scheme for cluster-based WSNs, where network time is divided into multiple rounds. Each round contains a setup phase and a steady-state phase. In the setup phase, clusters are formed and mutual trusted relationships between cluster nodes are established through two proposed authentication protocols called SET-μTESLA (Setup μTESLA) and SET-SCHNORR (Setup SCHNORR). SET-μTESLA uses the authenticated broadcast μTESLA scheme and reduces the iterations of hash function to improve the energy efficiency, while SET-SCHNORR applies a lightweight signature SCHNORR scheme. In the steady-state phase, collected environmental or physical data are transmitted through the proposed STEADY-μTESLA protocol (Steady-state μTESLA), which guarantees the data credibility. The analysis demonstrates that the security scheme does well in resisting such attacks as data confidentiality attacks, data integrity attacks and compromise node attacks. In addition, the results of calculations and simulations show that the proposed scheme performs better than the existing security protocols in terms of communication overhead and energy consumption.

Exploration of Wireless Sensor Network Based on Cloud Computing

To realize the exploration of wireless sensor network (WSN) based on cloud computing, the application service of WSN is taken as the starting point, the resource advantage of the cloud platform is used, and a WSN service framework based on cloud environment is proposed. Based on this framework, the problems of data management and reconstruction, network coverage optimization and monitoring, and edge recognition of holes are solved. In view of the node deployment of WSN and coverage problem of operation and maintenance optimization, the genetic algorithm is used to adjust the dormancy and energy state of nodes, and a parallel genetic algorithm for covering optimization in the cloud environment is proposed. For the operation and maintenance requirements of WSN, a parallel data statistics method for network monitoring is proposed. The experimental results show that the parallel algorithm is greatly improved in terms of the accuracy and time efficiency.

Estimation of Clock Skew for Time Synchronization Based on Two-Way Message Exchange Mechanism in Industrial Wireless Sensor Networks

Time synchronization is indispensable for convenient network management, device monitoring, security, and other fundamental operations in industrial wireless sensor networks (IWSNs). Over the past few decades, a wide variety of highly accurate clock synchronization protocols have been investigated by employing powerful statistical signal processing techniques. However, most two-way exchange estimation schemes do not readjust the node's local clock upon every resynchronization before the clock parameters are estimated. And it may not be appropriate in IWSNs where time synchronization is consistently required. Based on the two-way message exchange mechanism, this paper investigates the clock synchronization schemes of active node and overhearing node with immediate clock readjustment. The maximum-likelihood estimators of the clock skew and the corresponding Cramer–Rao lower bounds are derived assuming Gaussian delays. Simulation and experimental results validate the performance of the proposed estimators.

Moth Flame Optimization Based Energy Efficient Stable Clustered Routing Approach for Wireless Sensor Networks

The widespread use of wireless sensor devices and their advancements in terms of size, deployment cost and user friendly interface have given rise to many applications of wireless sensor networks (WSNs). WSNs need to utilize routing protocols to forward data samples from event regions to sink via minimum cost links. Clustering is an efficient data aggregation method that effectively reduces the energy consumption by organizing nodes into groups. However, in clustering protocols, CH has to bear an additional load for coordinating various activities within the cluster. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for the long run operation of WSN. Clustering a network with proper load balancing is an NP-hard problem. To solve such problems having vast search area, optimization algorithm is the preeminent possible solution. In this paper, moth flame optimization (MFO) based threshold-sensitive energy-efficient clustering protocol (TECP) is proposed to extend the stability period of the network. Multi-hop communication between CHs and BS is utilized using MFO to achieve optimal link cost for load balancing of distant CHs and energy minimization. Analysis and simulation results demonstrate that the proposed methodology significantly outperforms existing protocols in terms of energy consumption, system lifetime and stability period.

Novel energy-efficient secure routing protocol for wireless sensor networks with Mobile sink

The energy consumption and the Quality of Service (QoS) are the major concern in Wireless Sensor Network (WSN). Dissimilar sensor nodes in the heterogeneous network are the efficient network strategy. Since it has some capabilities like enhanced processing ability, additional memory power and distant transmission ability. For effective clustering and route formation among the pair of nodes, an efficient Enhanced Fuzzy C means and Adaptive TDMA Scheduling (ECATS) method is proposed as a protocol to facilitate communication within the network. So that data packets can be delivered within time to the mobile sink. Here introducing the novel protocol named as Neural Elliptic Galois (NEG) cryptography for efficient data security. Also, the location privacy (Threshold fault node detection) is taken in to account for better security. Cluster head (CH) selection is done on the basis of energy to manage the data aggregation among a number of nodes in the network. Here, hybridization of TDMA based Ant Lion Optimization scheduling is introduced for optimal CH selection is used for better energy efficiency. Finally, ECATS can be done with optimized WSN performance metrics such as packet delivery ratio, throughput, minimum energy consumptions, communication overheads & end to end delay. Thus, we can increase the reliability of the network while minimizing the energy consumption and the results are compared with few existing routing protocols using MATLAB simulation tool.

Heterogeneity consideration in wireless sensor networks routing algorithms: a review

Wireless sensor networks (WSNs) are resource-constrained networks with limited battery power, computation abilities and communication capabilities at the sensor node level. A long-standing need in the area is to reduce energy consumption of the WSN nodes and to extend the network lifetime, along with improving the network reliability and the network quality. Energy-efficient routing protocols can improve the WSN performance, and the algorithms have been explored widely by the researchers. In the majority of the work, sensor nodes have been considered homogeneous in their configuration and capabilities. However, in practical scenarios and with the advent of an era of connected devices, called "Internet of Things," consideration of node heterogeneity in WSN has become inevitable. Nodes' heterogeneity can be exploited constructively in WSN routing algorithms. In the literature, many different heterogeneous WSN scenarios (e.g., energy, computation, link heterogeneities) have been considered and the routing algorithms have been proposed for performance improvement in such scenarios. This paper focuses on the routing concepts for diverse heterogeneous WSNs scenarios and covers the state of the art in the area. The clustering-based approaches for routing decisions are extensively covered under different heterogeneous WSN scenarios. Along with presenting a comprehensive review of the routing algorithms under different varieties of heterogeneous WSNs, the paper also discusses the effects and interdependencies of different heterogeneities in routing decisions and unveils new research directions in the area.

Supercomputer Modeling of Large-Scale Wireless Sensor Networks

We consider a wireless sensor network (WSN) in the form of a chain of sensors with several sinks. Chiasserini–Garetto conditions are imposed on the functioning of the network nodes and the transmission environment. The number of network nodes is assumed in the thousands. A parallel simulation model is constructed to estimate the WSN performance parameters. Computer experiments conducted on a BlueGene/P supercomputer show good scalability of the model.

Designing prefix code to save energy for wirelessly powered wireless sensor networks

In the Internet of Things, wireless sensor networks (WSNs) are widely deployed. In recent years, wirelessly powered WSNs or battery-free WSNs (BF-WSNs), in which the nodes harvest energy from radio signals in the environment, have been emerging to support sustainable operation for WSNs. It is significant to design an energy-efficient data delivery scheme for the BF-WSNs. In this study, the authors propose the prefix code based scheme (PCBS) to save energy in data delivery by making use of the energy consumption disparity (ECD) between transmitting/receiving bit 0 and bit 1 in the existing non-modulation baseband transmission or carrier-modulation based passband transmission. The authors formulate an optimisation problem and use genetic algorithm to find its solution so that the energy-efficient prefix codebook is obtained. The codebook dilutes the ECD by containing the energy-consuming bit as few as possible, and the PCBS maps each m-bit data block into a prefix codeword in the codebook to conduct energy-efficient transmission at the transmitter and vice versa at the receiver. Both the experiments on wireless identification sensing platform and the simulations demonstrate that the proposed PCBS outperforms the existing schemes in terms of energy saving.

Towards Low-Cost Yet High-Performance Sensor Networks by Deploying a Few Ultra-fast Charging Battery Powered Sensors.

The employment of mobile vehicles to charge sensors via wireless energy transfer is a promising technology to maintain the perpetual operation of wireless sensor networks (WSNs). Most existing studies assumed that sensors are powered with off-the-shelf batteries, e.g., Lithium batteries, which are cheap, but it takes some non-trivial time to fully charge such a battery (e.g., 30–80 min). The long charging time may incur long sensor dead durations, especially when there are many lifetime-critical sensors to be charged. On the other hand, other studies assumed that every sensor is powered with an ultra-fast charging battery, where it only takes some trivial time to replenish such a battery, e.g., 1 min, but the adoption of many ultra-fast sensors will bring about high purchasing cost. In this paper, we propose a novel heterogeneous sensor network model, in which there are only a few ultra-fast sensors and many low-cost off-the-shelf sensors. The deployment cost of the network in the model is low, as the number of ultra-fast sensors is limited. We also have an important observation that we can significantly shorten sensor dead durations by enabling the ultra-fast sensors to relay more data for lifetime-critical off-the-shelf sensors. We then propose a joint charging scheduling and routing allocation algorithm, such that the longest sensor dead duration is minimized. We finally evaluate the performance of the proposed algorithm through extensive simulation experiments. Experimental results show that the proposed algorithm is very promising and the longest sensor dead duration by it is only about 10% of those by existing algorithms.