Performance Of Wireless Sensor Networks Research Articles

Relay Selection Joint Consecutive Packet Routing Scheme to Improve Performance for Wake-Up Radio-Enabled WSNs

Reducing energy consumption, increasing network throughput, and reducing delay are the pivot issues for wake-up radio- (WuR-) enabled wireless sensor networks (WSNs). In this paper, a relay selection joint consecutive packet routing (RS-CPR) scheme is proposed to reduce channel competition conflicts and energy consumption, increase network throughput, and then reduce end-to-end delay in data transmission for WuR-enabled WSNs. The main innovations of the RS-CPR scheme are as follows: (1) Relay selection: when selecting a relay node for routing, the sender will select the node with the highest evaluation weight from its forwarding node set (FNS). The weight of the node is weighted by the distance from the node to sink, the number of packets in the queue, and the residual energy of the node. (2) The node sends consecutive packets once it accesses the channel successfully, and it gives up the channel after sending all packets. Nodes that fail the competition sleep during the consecutive packet transmission of the winner to reduce collisions and energy consumption. (3) Every node sets two thresholds: the packet queue length threshold Nt and the packet maximum waiting time threshold Tt. When the corresponding value of the node is greater than the threshold, the node begins to contend for the channel. Besides, to make full use of energy and reduce delay, the threshold of nodes which are far from sink is small while that of nodes which are close to sink is large. In such a way, nodes in RS-CPR scheme will select those with much residual energy, a large number of packets, and a short distance from sink as relay nodes. As a result, the probability that a node with no packets to transmit becomes a relay is very small, and the probability that a node with many data packets in the queue becomes a relay is large. In this strategy, only a few nodes in routing need to contend for the channel to send packets, thereby reducing channel contention conflicts. Since the relay node has a large number of data packets, it can send many packets continuously after a successful competition. It also reduces the spending of channel competition and improves the network throughput. In summary, RS-CPR scheme combines the selection of relay nodes with consecutive packet routing strategy, which greatly improves the performance of the network. As is shown in our theoretical analysis and experimental results, compared with the receiver-initiated consecutive packet transmission WuR (RI-CPT-WuR) scheme and RI-WuR protocol, the RS-CPR scheme reduces end-to-end delay by 45.92% and 65.99%, respectively, and reduces channel collisions by 51.92% and 76.41%. Besides, it reduces energy consumption by 61.24% and 70.40%. At the same time, RS-CPR scheme improves network throughput by 47.37% and 75.02%.

A Trust-based Dynamic Slicing Mechanism for Wireless Sensor Networks

Abstract The security of data is an important research challenge in Wireless Sensor Networks (WSNs), and the Secure Data Aggregation (SDA) provide a feasible solution to prevent the networks from being compromised. In this paper, we propose a Trust-based Dynamic Slicing Mechanism (TDSM) to improve the performance of WSN. By establishing a trust evaluation mechanism, TDSM calculates the trust value of each node in the corresponding time interval, and according to this trust value, we carry out Cluster Head (CH) selection and prevent the untrusted node from being selected as a CH. Meanwhile, a slicing scheme is discussed and the sensing data are divided into different slices according to the trust of node. The simulation results show that TDSM can defend against various attacks and promote the performance of WSNs.

Consensus-Based Clock Synchronization in Wireless Sensor Networks With Truncated Exponential Delays

Clock synchronization is a significant basis for many operations in wireless sensor networks. The distributed consensus-based clock synchronization has gained popularity for its robustness and scalability. However, most consensus-based clock synchronization protocols either ignore communication delay, or consider bounded delay without distribution model. In this article, based on the consensus theory, the clock synchronization problem with truncated exponential delay is investigated. We use the maximum likelihood method to estimate relative drift between nodes. A recursive method is presented to cope with the maximum likelihood estimation, which largely reduces the computational complexity and the storage overhead. Then, the estimated relative drift is adopted in the two main parts of consensus clock synchronization: drift compensation and offset compensation. We give the rigorous theoretical proof of the convergence of the network-wide synchronization. Simulations further verify the theoretical analysis and show that the synchronization performance of the proposed algorithm is better than existing similar schemes under truncated exponential delays.

NLFFT: A Novel Fault Tolerance Model Using Artificial Intelligence to Improve Performance in Wireless Sensor Networks

A wireless sensor network (WSN) is a collection of various tiny devices known as sensor nodes, which are also called motes. Due to high-energy consumption, the possibility of hardware, link or node failure, and some malicious attacks, sensor networks are considered error-prone networks. Hence, fault tolerance (FT) in WSN is one of the prominent issues. This article presents a novel FT approach named node-link failure fault tolerance model (NLFFT Model) in WSN, to handle the faults that occur either by link or node failure during data transmission from the sensor to the sink or base station. The NLFFT model consists of an improved quadratic minimum spanning tree (Imp-QMST) approach. This approach helps in finding the alternate link whenever it fails due to various situations and also an improved-handoff (Imp-Handoff) algorithm to support the node failure to the fault tolerance. Improved QMST presents a novel mechanism to find an alternate edge in place of the broken or failed edge in the spanning tree, to improve the fault tolerance in WSN. Imp-Handoff suggests a novel way to find the faulty node owing to less battery power and replaces a defective node by an appropriate neighbor to shift the tasks performed by a faulty node in WSN. Simulation results clearly state that as compared to the basic techniques i.e. Q-MST and Handoff algorithm, the proposed NLFFT model improvises the performance of WSN around by 7%. The results prove that the Imp-QMST gives about 6% improved throughput, 5% less end-to-end delay, and 6% less power consumption than the QMST algorithm. Similarly, Imp-Handoff improves about 4% throughput, 6% less end-to-end delay, and utilizes 7% less power consumption.

Enhancing Performance Of WSN By Utilizing Secure Qos Based Explicit Routing

Wireless sensor networks (WSN) are infrastructure less and self-configured a wireless network that allows monitoring the physical conditions of an environment. Many researchers focus on enhancing the performance of WSN in order to provide effective delivery of data on the network, but still results in lower quality of services like energy consumption, delay and routing. We tackle this problem by introducing a new routing algorithm, QoS-based explicit routing algorithm which helps in transmitting the data from source node to destination node on WSN. We also involve clustering process in WSN based on genetic algorithm and particle swarm optimisation (GA and PSO) algorithm. We proposed identity-based digital signature (IBDS) and enhanced identity-based digital signature (EIBDS) that involves reduction of computation overhead and also increasing resilience on the WSN. We also use advanced encryption standard (AES), for ensuring the security between nodes and avoid hacking of data by other intruders.

Performance evaluation of main approaches for determining optimal number of clusters in wireless sensor networks

Among important issues in the current energy-efficient routing protocols for wireless sensor networks (WSNs) based on clustering approaches are how to determine the optimal number of clusters, how to generate clusters and how select cluster-heads to improve WSNs performance. These approaches aim to reduce energy consumption of the WSN nodes and to extend the network lifetime. This paper reviews and compares the performance of three clustering techniques for determining number of clusters: rule of thumb, elbow, and silhouette. Each of these techniques involves a distributed K-means approach to generate clusters. Moreover, we compare these three clustering methods with low energy adaptive clustering hierarchy (LEACH), Imp LEACH and MODLEACH in terms of network lifetime, energy consumption and the number of packets sent to the base station (BS). The results obtained indicate that rule of thumb method provides better performance compared to other clustering techniques in terms of energy consumption and network lifetime.

Distributed multi-hop routing algorithm for wireless sensor networks

In a Wireless Sensor Network (WSN), routing is the process of finding a cost effective route in terms of power consumption. As an evaluation criterion for the WSN performance, network lifetime is directly affected by the routing method. In a wide variety of WSNs, different techniques are used as routing methods, such as shortest distance path. In this paper, we propose a novel algorithm, optimizing power consumption in WSN nodes, based on the shortest path algorithm. In this approach, the energy level of nodes and their geographical distance from each other contribute to the weight of the connecting path. The proposed algorithm is used as a data dissemination method in WSNs with randomly scattered nodes. We also apply Dijkstra?s shortest path algorithm to the same networks. The results showed that the proposed algorithm increases the network lifetime up to 30 % by preventing nodes with low charge levels from early disconnection.

Performance evaluation of main approaches for determining optimal number of clusters in wireless sensor networks

Among important issues in the current energy-efficient routing protocols for wireless sensor networks (WSNs) based on clustering approaches are how to determine the optimal number of clusters, how to generate clusters and how select cluster-heads to improve WSNs performance. These approaches aim to reduce energy consumption of the WSN nodes and to extend the network lifetime. This paper reviews and compares the performance of three clustering techniques for determining number of clusters: rule of thumb, elbow, and silhouette. Each of these techniques involves a distributed K-means approach to generate clusters. Moreover, we compare these three clustering methods with low energy adaptive clustering hierarchy (LEACH), Imp LEACH and MODLEACH in terms of network lifetime, energy consumption and the number of packets sent to the base station (BS). The results obtained indicate that rule of thumb method provides better performance compared to other clustering techniques in terms of energy consumption and network lifetime.

Sustaining the Life of Wireless Sensor Node with Energy Harvesting

Wireless Sensor Networks (WSN) play a significant role in a number of sensing and monitoring applications. It has also become a key enabling technology for Internet of Things (IoT). Most of the earlier works were focused on extending the battery life by reducing the energy consumption of the wireless node. Availability of small footprint devices capable of ambient energy harvesting has triggered renewed interest in enhancing the performance of WSNs. In this paper we propose a novel method that can be applied to duty cycle based Media Access Control (MAC) protocols in energy harvesting WSNs to sustain the life of the node. Our approach takes into consideration the solar energy available at any instant of time so as to maximize the performance. We have conducted detailed measurements and analysis of solar energy harvested under different conditions. Our results show significant enhancement in throughput achieved while sustaining the life of the node.

Minimal Path-Based Reliability Model for Wireless Sensor Networks With Multistate Nodes

Wireless sensor networks (WSNs) find application in various fields like environmental monitoring, health-care, land security, and many more. To ease our day-to-day activity, WSNs have become an integral tool for complex data gathering tasks. Monitoring a phenomenon by a WSN depends on the collective data provided by the sensor nodes. To ensure reliable operation of WSNs, it is important to quantify the performance of such networks in terms of network reliability measures. This article studies the reliability of WSNs with multistate nodes and proposes an approach to evaluate the flow-oriented network reliability of WSNs consisting of multistate sensor nodes. The proposed method takes into account the dynamic state of the network due to multistate sensor nodes. The proposed approach includes enumeration of shortest minimal paths from application-specific flow satisfying sensor nodes (source nodes) to the sink node. It then proposes a modified sum-of-disjoint products approach to evaluate WSN reliability in the presence of multistate nodes from the enumerated shortest minimal paths. Simulations are performed on WSNs of various sizes to show the applicability of the proposed approach on arbitrary WSNs.

ISleep: thermal entropy aware intelligent sleep scheduling algorithm for wireless sensor network

The optimal operation of Wireless Sensor Network (WSN) requires a sustainable topology. A dead node may result in a breach of end-to-end connectivity in the network. The thermal sensitivity of any sensor affects the lifetime of the node. Every sensor node has a typical operating temperature zone. Beyond the operating temperature threshold, the system reduces the performances and consequently forms a disconnected network. Network entropy represents the evaluation of the anisotropy of the temperature distribution profile of the system that measures the network stability. In this work, an intelligent sleep scheduling algorithm ‘iSleep’ is proposed based on available neighboring nodes, associated network entropy, and traffic flow pattern. iSleep enables the sensor nodes to auto-control the sleep state of the nodes and maintain network connectivity for a longer amount of time. The proposed algorithm is tested for numerous representative networks on MATLAB 2016a and the Cooja simulator on Instant Contiki-2.7 with two energy models of Tmote Sky and Zolertia Z1. It is found that the proposed algorithm outperforms the existing scheduling algorithms in terms of the lifetime of a network for both the energy models.

UAV-Assisted RFET: A Novel Framework for Sustainable WSN

Limited battery capacity is one of the major hurdles towards perpetual operation of wireless sensor networks. In this paper, a novel framework for charging the sensor nodes using unmanned aerial vehicle (UAV)-assisted radio frequency energy transfer (RFET) is presented. First, the notion of RFET zone is conceptualized and a closed-form expression for RFET zone radius is obtained. The sensor nodes located inside this zone can harvest energy from the transmitter mounted on UAV. The effective power harvested at the sensor node situated at different spatial locations is evaluated by considering the impact of shadowing statistics of path loss and non-linear RF-to-direct current conversion efficiency. With these findings on sensor nodes deployed in a given area, an optimization problem is formulated with the objective of minimizing the total time in a charging cycle, which is comprised of travel time and charging time. This problem is decomposed into two sub-problems and they are solved individually in sequential steps. The optimal solution of the first sub-problem, which provides the sequence of charging having minimum travel time, is a Traveling Salesman Problem (TSP). In the second sub-problem, the presence of Lambert function makes it analytically intractable, and hence, approximations are presented to solve this. Subsequently, to account for the health parameters of the sensor nodes in estimating the charging cycle, three variants of order of charging, namely, Voltage-aware Charging Sequence , Operational Time-aware Charging Sequence , and Iterative Charging Sequence , are proposed. Through system simulations it is demonstrated that, in a generalized setting, the charging sequence offered by the proposed variants perform increasingly better in comparison to the state-of-the-art TSP approach.

A Stochastic Game Approach for Collaborative Beamforming in SDN-Based Energy Harvesting Wireless Sensor Networks

Collaborative beamforming (CB) has recently emerged as a promising technique for transmission range extension and energy consumption reduction in wireless sensor networks (WSNs). However, due to the constrained energy and limited data processing capabilities of sensor nodes, the performance optimization of CB mainlobe and sidelobe control (SC) encounters challenges in the practical deployment. To address these challenges, we present an architecture of software-defined energy harvesting WSN (SD-EHWSN) for CB communications. Specifically, we first design the mechanism of CB communications based on the software-defined network (SDN) architecture to reduce the communication and computational overhead of sensor nodes. Then, we consider solar energy-harvesting system to achieve long-term operation of WSN and utilize a stationary Markov (SM) chain to model the arrival process of solar energy. Based on the stochastic nature of solar energy, a stochastic game model is developed to formulate the problem of CB optimization in SD-EHWSN, and the existence proof of Nash equilibria is provided. Based on the analytical results, we propose a reinforcement learning algorithm to maximize the long-term signal-to-noise ratio (SNR) performance with SC and prove the convergence of the algorithm. Simulation results are presented to validate the efficiency of the proposed scheme for CB communications in SD-EHWSN.

On the performance of quick artificial bee colony algorithm for dynamicdeployment of wireless sensor networks

In recent years, the use of wireless sensor networks (WSNs) has increased and there have been significant improvements in this field. Especially with smarter, cheaper, and smaller sensor nodes, various kinds of information can be detected and collected in different environments and under different conditions. WSNs have thus been used in many applications such as military, surveillance, target tracking, home, medical, and environmental applications. As the popularity of WSNs increases, problems related to these networks are being realized. The dynamic deployment problem is one of the main challenges that have a direct effect on the performance of WSNs. In this study, a novel optimization technique named the quick artificial bee colony (qABC) algorithm was applied to the dynamic deployment problem of WSNs. qABC is a new version of the artificial bee colony algorithm (ABC) and it redefines the onlooker bee phase of ABC in a more detailed way. In order to see the performance of qABC on this problem, WSNs that include only mobile sensors or both stationary and mobile sensors were considered with binary and probabilistic detection models. Some experimental studies were conducted for tuning the colony size ($CS$) and neighborhood radius ($r$) parameters of the qABC algorithm, and the performance of the proposed method was compared with the standard ABC algorithm and some other recently introduced approaches including a parallel ABC, a cooperative parallel ABC, a version of ABC powered by a transition control mechanism (tlABC), and a parallel version of tlABC. Additionally, some CPU time analyses were provided for qABC and ABC considering different dimensions of the problem. Simulation results show that the qABC algorithm is an effective method that can be used for the dynamic deployment problem of WSNs, and it generally improves the convergence performance of the standard ABC on this problem when $r \geq 1$.

Restoring Connectivity of Damaged Sensor Networks for Long-Term Survival in Hostile Environments

Connectivity restoration plays an important role in maintaining the long-term operation in wireless sensor networks (WSNs), especially, in environment-harsh cases. However, current solutions lack the ability to handle the second damages and the capacity of designing requirement-different connectivity approaches according to different needs. In this article, we propose a durability-based connectivity establishment (DBCE) scheme for disjoint segments of WSNs. This scheme includes three approaches regarding segment evaluation or segment selection: 1) a segment shape evaluation approach; 2) a region different connectivity approach; and 3) a data traffic transfer approach, for their respective objectives. The unique characteristics of this article are twofold. On the one hand, this is the first attempt to investigate segment shapes, which we demonstrate have great impact on the robustness of the network. On the other hand, distinguished from the existing networks with uniform connectivity rule, the network is divided into two parts and different connectivity sequences and connectivity approaches are designed according to disparate features and requirements of the network. The performance of DBCE is validated through extensive simulation experiments.

Effective TDMA scheduling for tree-based data collection using genetic algorithm in wireless sensor networks

Data collection is a major operation in Wireless Sensor Networks (WSNs) and minimizing the delay in transmitting the collected data is critical for a lot of applications where specific actions depend on the required deadline, such as event-based mission-critical applications. Scheduling algorithms such as Time Division Multiple Access (TDMA) are extensively used for data delivery with the aim of minimizing the time duration for transporting data to the sink. To minimize the average latency and the average normalized latency in TDMA, we propose a new efficient scheduling algorithm (ETDMA-GA) based on Genetic Algorithm(GA). ETDMA-GA minimizes the latency of communication where two dimensional encoding representations are designed to allocate slots and minimizes the total network latency using a proposed fitness function. The simulation results show that the performance of the proposed algorithm outperforms the existing state-of-the-art approaches such as Rand-LO, Depth-LO, DepthRe-LO, IDegRe-LO, and IDeg-LO in terms of average latency, average normalized latency, and average schedule length.

Efficient Mobile Energy Replenishment Scheme Based on Hybrid Mode for Wireless Rechargeable Sensor Networks

Wireless charging is a greatly promising technology to resolve the energy limitation problem suffered by wireless sensor networks. In this paper, we propose an efficient mobile energy replenishment scheme based on hybrid mode, namely MERSH, which merges the advantages of the existing online and offline modes to improve the energy supplement efficiency and meanwhile to adapt well to the energy consumption dynamicity of sensors to achieve sustainable operation of wireless sensor networks. In MERSH, nodes are allowed to proactively send charging requests to the mobile charger, called MC, when the residual energy goes down a lower level. With a special-designed low-cost method, MERSH can plan an optimized path for MC every time when it charges the sensors that have sent charging requests. Moreover, to ensure energy supplement and avoid node failure due to the high dynamic energy consumption, MERSH dynamically adjusts the charging duration of each node along the calculated charging path according to its maximum tolerate charging latency and minimum charging wait time. The theoretic analysis shows that MERSH guarantees MC to move along the optimized charging path, which reduces the whole charging cost. Through extensive simulation results, we validate the effectiveness of the proposed MERSH.

Energy-efficient routing model for water pipeline monitoring based on wireless sensor networks

In wireless sensor networks (WSNs), water pipeline monitoring is one of the domain applications that require lower energy consumption. Sensor nodes have to sense environment parameters and route the leak data to a central node in low power consumption. Several routing techniques have been proposed to enhance the network lifetime in WSNs. In this paper, we propose a heterogeneous two-tiered routing model in low power consumption. Two routing algorithms are applied in each tier of this model. To evaluate the performance capability of the proposed solution, comparison results based on the simulation tool Cooja applied in the WSN operating system Contiki are presented. We detail, also, a new prototyping based on several platforms to validate the proposed approach. This prototype is experienced in a real water pipeline testbed designed for our application.

Energy-Neutral Operation Based on Simultaneous Wireless Information and Power Transfer for Wireless Powered Sensor Networks

For energy-neutral operation (ENO) of wireless sensor networks (WSNs), we apply a wireless powered communication network (WPCN) to a WSN with a hierarchical structure. In this hierarchical wireless powered sensor network (WPSN), sensor nodes with high harvesting energies and good link budgets have energy remaining after sending their data to the cluster head (CH), whereas the CH suffers from energy scarcity. Thus, we apply the simultaneous wireless information and power transfer (SWIPT) technique to the considered WPSN so that the sensor nodes can transfer their remaining energy to the CH while transmitting data in a cooperative manner. To maximize the achievable rate of sensing data while guaranteeing ENO, we propose a novel ENO framework, which provides a frame structure for SWIPT operation, rate improvement subject to ENO, SWIPT ratio optimization, as well as clustering and CH selection algorithm. The results of extensive simulations demonstrate that the proposed ENO based on SWIPT significantly improves the achievable rate and reduces the energy dissipated in the network while guaranteeing ENO, in comparison with the conventional schemes without SWIPT.

Cluster Based Data Aggregation Scheme for Latency and Packet Loss Reduction in WSN

In Wireless Sensor Networks (WSN), the main issues of cluster based data aggregation algorithms are energy balancing, packet loss and latency reduction. In existing scheduling algorithms for data aggregation, time slots are mostly assigned based on the data sensing period and data transmission rate, ignoring packet loss and latency. In this paper, Cluster based Data Aggregation Scheme for Latency and Packet Loss Reduction in WSN is proposed. The proposed scheme consists of two phases: Aggregation Tree Construction and Slot scheduling algorithm. In phase-1, each cluster head applies compressive aggregation for the data received from its members. Then the aggregation tree is constructed by the sink using Minimum Spanning Tree (MST). In phase-2, the packet loss rate and latency are taken into consideration while prioritizing and assigning timeslots to the nodes with aggregated data. This scheme avoids using unnecessary retransmissions and waiting, which results to be beneficial in enhancing the network performance in WSN. Simulation results show that the proposed scheme reduces the latency and overhead and increases the packet delivery ratio and residual energy.