Heterogeneous Wireless Sensor Networks Research Articles

A performance efficient joint clustering and routing approach for heterogeneous wireless sensor networks

AbstractWireless sensor networks have proven to be a promising paradigm due to their wide applicability in the real‐world scenarios. Because of the resource constraint, type‐variant and battery‐powered features of the sensor nodes, the key challenge is how to improve the energy usage and network life in the heterogeneous sensor networks. In this paper, the Mahalanobis distance based K‐Means algorithm is integrated with a novel evolutionary approach for clustering called calf search optimization algorithm (K‐CSOA) that creates energy efficient clusters. The routing in the network is performed using a power efficient ant colony optimization algorithm (ACO). The extensive and multiple simulations are performed to validate the effectiveness of the proposed method. It is observed that the running time of the proposed scheme is lowered due to the implementation of hybrid clustering. The results are validated for various performance metrics wherein the proposed method shows 41.6% and 4.3%–12% reduction in energy expenditure and end‐to‐end delay respectively and around 60% enhancement in the throughput of the network when compared with other similar state‐of‐the‐art methods. In addition to this, statistical analysis is also carried out to identify the performance of the proposed algorithm.

Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method

The deployment of the sensor nodes (SNs) always plays a decisive role in the system performance of wireless sensor networks (WSNs). In this work, we propose an optimal deployment method for practical heterogeneous WSNs which gives a deep insight into the trade-off between the reliability and deployment cost. Specifically, this work aims to provide the optimal deployment of SNs to maximize the coverage degree and connection degree, and meanwhile minimize the overall deployment cost. In addition, this work fully considers the heterogeneity of SNs (i.e. differentiated sensing range and deployment cost) and three-dimensional (3-D) deployment scenarios. This is a multi-objective optimization problem, non-convex, multimodal and NP-hard. To solve it, we develop a novel swarm-based multi-objective optimization algorithm, known as the competitive multi-objective marine predators algorithm (CMOMPA) whose performance is verified by comprehensive comparative experiments with ten other state-of-the-art multi-objective optimization algorithms. The computational results demonstrate that CMOMPA is superior to others in terms of convergence and accuracy and shows excellent performance on multimodal multi-objective optimization problems. Sufficient simulations are also conducted to evaluate the effectiveness of the CMOMPA based optimal SNs deployment method. The results show that the optimized deployment can balance the trade-off among deployment cost, sensing reliability and network reliability. The source code is available on https://github.com/iNet-WZU/CMOMPA .

A new Asymmetric Link Quality Routing protocol (ALQR) for heterogeneous WSNs

Wireless sensor networks (WSNs) have received a lot of attention due to numerous advancements, especially in smartphones, smart watches, smart homes, and even smart cities. Aside from WSNs progression, they are affected by a combination of factors, including network efficiency, link asymmetry, and energy loopholes. This paper introduces a novel approach, particularly useful for challenging receiver-initiated asynchronous heterogeneous networks, which are competitive due to low energy consumption. In WSNs, inefficient energy consumption is a widespread issue that drastically decreases the lifetime of each deployment network. To overcome these challenges, we proposed an intelligent Asymmetry link Quality Routing (ALQR) protocol for WSNs with a low energy consumption clustering protocol. Additionally, If there is no cross-channel traffic, ALQR provides adequate connection control at a low cost, making it more advantageous to WSN applications. We investigated the effectiveness of our proposed protocol compare to multiple state-of-the-art clustering-based protocols. According to the findings, ALQR outperforms in terms of region and lifetime, while ALQR ensures overall throughput.

Intelligent Deployment Strategy for Heterogeneous Nodes to Increase the Network Lifetime of Wireless Sensor Networks

The applications of wireless sensor networks are increased day by day for different applications. Heterogeneous wireless sensor networks offer limitless possibilities because to their expandable capabilities, such as diverse computing power and sensing range, but they also represent significant issues due to the scarcity of energy, which is typically non-renewable. The node deployment, coverage area, connectivity, power depletion and network life time are major issues in wireless sensor networks. We have deployed heterogeneous sensor nodes on the basis of energy to design a heterogeneous network model and applied intelligent node deployment techniques by using metaheuristic algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) algorithm to minimize the power depletion of sensor nodes for enhancing network life time using multi-hop transmission in this paper. The benefits of heterogeneity have been revealed by our experimental findings.

Hybrid Clustering and Routing Algorithm with Threshold-Based Data Collection for Heterogeneous Wireless Sensor Networks.

The concept of the internet of things (IoT) motivates us to connect bulk isolated heterogeneous devices to automate report generation without human interaction. Energy-efficient routing algorithms help to prolong the network lifetime of these energy-restricted smart devices that are connected by means of wireless sensor networks (WSNs). Current vendor-level advancements enable algorithm-level flexibility to design protocols to concurrently collect multiple application data while enforcing the reduction of energy expenditure to gain commercial success in the industrial stage. In this paper, we propose a hybrid clustering and routing algorithm with threshold-based data collection for heterogeneous wireless sensor networks. In our proposed model, homogeneous and heterogeneous nodes are deployed within specific regions. To reduce unnecessary data transmission, threshold-based conditions are presented to prevent unnecessary transmission when minor or no change is observed in the simulated and real-world applications. We further extend our proposed multi-hop model to achieve more network stability in dense and larger network areas. Our proposed model shows enhancement in terms of load balancing and end-to-end delay as compared to the other threshold-based energy-efficient routing protocols, such as the threshold-sensitive stable election protocol (TSEP), threshold distributed energy-efficient clustering (TDEEC), low-energy adaptive clustering hierarchy (LEACH), and energy-efficient sensor network (TEEN).

Efficient routing and performance amelioration using Hybrid Diffusion Clustering Scheme in heterogeneous wireless sensor network

SummaryHeterogeneous wireless sensor networks (HWSNs) have a wide range of application in the contemporary world particularly in sensing and hunting of events without human interference. Due to this limitation of network resources and energy of sensor nodes, the contention of available resources for transmission will increase the energy consumption in the network and degrade the performance of Quality of Service. During dense traffic conditions, sensor nodes in close proximity to base station (BS) faces severe bottleneck resulting in congestion and energy hole problem. The efficient utilization of energy and packet loss is due to link break and by traffic analysis attack is a primary threat in HWSN applications. To sustain maximum energy in the network, two important aspects are considered, namely, flawless selection of path and concise clustering. This paper describes about a multi‐objective‐based Hybrid Diffusion Clustering Scheme (HDCS) through Jellyfish routing protocol with Time Division Multiple Access (JRP‐TDMA)‐based routing method in HWSN. Cluster head is elected based on multiple objectives such as residual energy, cost, and distance. Once CH selection is done, JRP‐TDMA based on available timeslot handpicks the best path for transmitting to BS. The proposed protocol is analyzed, simulated, and performance results are correlated with existing protocols namely, diffusion clustered routing protocol (DCRP), efficient unequal clustering (EEUC), Markov chain model‐based optimal cluster head (MOCH), and genetic algorithm (GA). The number of alive sensor nodes of proposed method has increased by 24.4% when compared with other approaches.

Energy-Efficient Deployment in Static and Mobile Heterogeneous Multi-Hop Wireless Sensor Networks

We study a heterogeneous wireless sensor network (WSN) where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> heterogeneous access points (APs) gather data from densely deployed sensors and transmit their sensed information to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> heterogeneous fusion centers (FCs) via multi-hop wireless communication. The heterogeneous optimal deployment of APs and FCs is modeled as an optimization problem with total wireless communication power consumption of the network as its objective function. We consider both static WSNs, where APs and FCs retain their deployed position, and mobile WSNs where APs and FCs can move from their initial deployment to their optimal locations. Based on the derived necessary conditions for the optimal deployment in static WSNs, we propose an iterative algorithm to deploy APs and FCs. In addition, we study the necessary conditions of the optimal movement-efficient deployment in mobile WSNs with constrained movement energy and present iterative algorithms to find such deployments, accordingly. Simulation results show that our proposed deployment algorithms outperform the existing methods in the literature, and achieve a lower total wireless communication power in both static and mobile WSNs.

Secure Routing-Based Energy Optimization for IoT Application with Heterogeneous Wireless Sensor Networks

Wireless sensor networks (WSNs) and the Internet of Things (IoT) are increasingly making an impact in a wide range of domain-specific applications. In IoT-integrated WSNs, nodes generally function with limited battery units and, hence, energy efficiency is considered as the main design challenge. For homogeneous WSNs, several routing techniques based on clusters are available, but only a few of them are focused on energy-efficient heterogeneous WSNs (HWSNs). However, security provisioning in end-to-end communication is the main design challenge in HWSNs. This research work presents an energy optimizing secure routing scheme for IoT application in heterogeneous WSNs. In our proposed scheme, secure routing is established for confidential data of the IoT through sensor nodes with heterogeneous energy using the multipath link routing protocol (MLRP). After establishing the secure routing, the energy and network lifetime is improved using the hybrid-based TEEN (H-TEEN) protocol, which also has load balancing capacity. Furthermore, the data storage capacity is improved using the ubiquitous data storage protocol (U-DSP). This routing protocol has been implemented and compared with two other existing routing protocols, and it shows an improvement in performance parameters such as throughput, energy efficiency, end-to-end delay, network lifetime and data storage capacity.

EMEECP-IOT: Enhanced Multitier Energy-Efficient Clustering Protocol Integrated with Internet of Things-Based Secure Heterogeneous Wireless Sensor Network (HWSN)

In recent years, Internet of Things (IoT)-based constrained wireless sensor networks (WSN) have been observed with radical developments. The aligned phenomenon such as maximizing resource utilization and the delivery of efficient services is the need of the hour to suffice for the purpose of seamless connectivity. Typically, IoT devices have limited battery life and are deployed in remote areas. As a result, limited battery life limits the use of networks in some situations. To develop energy-efficient techniques for the IoT requires innovative ideas where the current techniques of using WSNs cannot directly apply to the IoT due to protocol issues, SLAs (service level agreements), scalability issues, and complexity levels. To overcome these issues, this article proposes an Enhanced Multitier Energy-Efficient Clustering Protocol integrated with the Internet of Things (EMEECP-IOT) based on the heterogeneous wireless sensor networks (HWSN) technique. The proposed approach has been compared with the current and standard protocols with respect to the parameters such as network stability, packets received, average throughput, standard deviation (SD), and residual energy. The simulation results show significantly better performance of the proposed EMEECP-IoT-based HWSN technique than the existing technique. Extensive performance factors show that the proposed EMEECP-IOT extends the network lifetime by 35% and decreases the network’s energy consumption by 21%. This proposed approach can be applied to save energy and enhance network lifetime for WSN and IoT-oriented applications.

Mobile Sink-Based Path Optimization Strategy in Heterogeneous WSNs for IoT Using Pigeon-Inspired Optimization Algorithm

Data collection is the basic purpose of deploying in heterogeneous WSNs for Internet of things, and the problem of data collection is the key problem that needs to be solved in heterogeneous WSNs. How to collect energy-efficient and reliable data is one of the key technologies of heterogeneous WSNs. Collecting the sensor node data by mobile sink is an effective measure to solve data collection efficiency. To this end, a data collection strategy of mobile sink for heterogeneous WSNs based on pigeon-inspired optimization by PSO algorithm is proposed. The proposed algorithm uses the improved pigeon-inspired optimization by particle swarm optimization algorithm to select the best dwell point and then regards the construction of the moving path based on the dwell point as a traveling salesman problem to optimize the moving path and solve the optimal moving path. The experimental analysis and simulation results show that, compared with other algorithms, the algorithm proposed in this paper can effectively prolong the lifetime of the network and reduce the delay of data collection, increasing the amount of data collection.

Retraction Note to: Optimal coverage along with connectivity maintenance in heterogeneous wireless sensor network

Retraction Note to: Optimal coverage along with connectivity maintenance in heterogeneous wireless sensor network

UAV-Assisted Data Collection for Dynamic and Heterogeneous Wireless Sensor Networks

To consider the heterogeneity of sensor nodes (SNs) and further protect the SNs with low residual energy, in this letter, we propose an energy-saving UAV-assisted data collection framework for a heterogeneous wireless sensor network (WSN), where the working mode of each SN is dynamically determined by its residual energy and a certain energy threshold. Our aim is to minimize the WSN’s energy consumption by jointly optimizing the energy threshold, SNs’ working modes and cluster head (CH) selection, while ensuring successful data transmission and satisfying UAV trajectory length constraint. To address it, we propose a one-dimensional search method to find the optimal energy threshold, and leverage a sequential rank-one constraint relaxation algorithm to select the CHs. Numerical results justify that the performance of our proposed approach is more energy-saving than the existing benchmarks.

An Analytical Model of Multi-intrusion Detection for Heterogeneous Wireless Sensor Networks Applied in the Monitoring Area

Aim: For a Wireless Sensor Network (WSN), the sensor node deployment is a critical issue since it reflects the coverage quality. It is the most fundamental issue in WSNs, and it has a great effect on the overall WSN application performance. When it is necessary to deploy randomly sensor nodes in a field of interest to form a WSN, ensuring a high coverage quality becomes difficult. In the random deployment scenario, the most difficult aspect of sensing coverage is to find how well the sensor nodes cover the field of interest. In this paper, we analyze the intrusion detection in a WSN which is defined as a mechanism for monitoring and detecting any intruder in a field of interest, with the objective of enhancing the detection quality in a random WSN. It is required to establish more specific measurements of node density and sensor range that impact the overall system performance especially in the intrusion detection application. To enhance the quality of intrusion detection, several probabilistic models are adopted for heterogeneous WSN in the random deployment scenario. Methods: Multi-intrusion detection model probability in a heterogeneous wireless sensor network for random deployment was used. Results and Discussion: In both homogeneous and heterogeneous WSNs, we analyzed our probabilistic model for multi-intrusion detection in single and multi-sensing detection. Conclusion: Our probabilistic models are useful in selecting the critical parameters of WSN in order to meet the detection quality requirement.

Performance Analysis of Burst Traffic Awareness Based Mobile Sink Routing Technique for Wireless Sensor Networks

In wireless sensor networks (WSNs), it is vital to adopt a suitable mobile routing algorithm between sensor nodes and mobile sinks (MSs) for data gathering efficiently. In WSNs, random mobility of the MSs increases the mobile path length in the network when data traffic bursts. Therefore, the focus of this study is to overcome burst traffic in an energy-efficient way using the MSs in the network. In this study, a new burst traffic awareness adaptive mobile routing scheme based on heterogeneous WSNs has been developed. The network area is divided into two cluster groups in the proposed scheme, each with a certain number of clusters. In the network, a MS of each cluster group acts. The MSs gather all data in a single-hop attitude as soon as they arrive at the clusters. In this way, the energy load is distributed evenly among the network. Once a burst data is detected in the routing model, a MS updates its trajectory to the cluster head (CH) where the burst occurs. The performance results validate that the proposed methodology outperforms recent studies based on the network lifetime, average energy consumption, and average mobile path length. Also, the effect of the burst traffic situations on network efficiency is analyzed with simulation.

IMPERIALIST COMPETITIVE ALGORITHM FOR INCREASING THE LIFETIME OF WIRELESS SENSOR NETWORK

Recent years have seen the rapid growth in the applications of wireless sensor network (WSN) which is due to the advances of sensor nodes with low cost and tiny size. Despite the various potential applications of WSN, one of the key tasks in sensor network design is to make sure that the network is functional as long as possible. This paper presents an energy-efficient cluster head selection algorithm for the clustering of heterogeneous WSN, inspired by Imperialist Competitive Algorithm (ICA). In order to reduce the network energy consumption and subsequently increases the sensor network lifetime, the clustering problem is transformed into an optimization problem and the specific cost function is used to select the cluster heads in a way that the energy utilization of the network is optimized. Extensive simulation works are done based on MATLAB to test the algorithm in various network scenarios, with different network sizes and number of nodes. Simulation results have shown that the proposed algorithm is able to extend the network lifetime compared to its comparative by up to 154 percent in terms of first node death. Furthermore, choosing the optimum set of cluster heads at every round has proved that our proposed algorithm not only could reduce the network energy consumption, but also improves the total data delivery at the base station up to 59 percent compared to the well-known algorithm.

A Multi-Strategy Improved Sparrow Search Algorithm for Solving the Node Localization Problem in Heterogeneous Wireless Sensor Networks

Aiming at the problems of slow convergence and low accuracy of the traditional sparrow search algorithm (SSA), a multi-strategy improved sparrow search algorithm (ISSA) was proposed. Firstly, the golden sine algorithm was introduced in the location update of producers to improve the global optimization capability of SSA. Secondly, the idea of individual optimality in the particle swarm algorithm was introduced into the position update of investigators to improve the convergence speed. At the same time, a Gaussian disturbance was introduced to the global optimal position to prevent the algorithm from falling into the local optimum. Then, the performance of the ISSA was evaluated on 23 benchmark functions, and the results indicate that the improved algorithm has better global optimization ability and faster convergence. Finally, ISSA was used for the node localization of HWSNs, and the experimental results show that the localization algorithm with ISSA has a smaller average localization error than that of the localization algorithm with other meta-heuristic algorithms.

FACS: Fairness aware clustering scheme for monitoring applications of internet of things based wireless sensor networks

Energy conservation is one of the main challenges hindering the advancement of Wireless Sensor Networks (WSNs) in the Internet of Things (IoT). Although various approaches including probabilistic, deterministic, and mixed model based solutions have been proposed to solve the issue of energy conservation in cluster based WSNs, most of them do not consider the node’s heterogeneity in terms of energy as well as transmission rate during the creation or selection of clusters. In this paper, a new Fairness Aware Clustering Scheme (FACS) for heterogeneous WSN is proposed, where we consider heterogeneity of the WS N in terms of transmission rate and energy. In contrast to existing schemes, the proposed FACS provides a set of polices that take care of round operations and improve the network performance and lifetime. In FACS, fuzzy logic is employed to benefit from the heterogeneity of the nodes and to determine the utility of each node. Through extensive simulation, we show that FACS performs efficiently and it outperforms the existing state-of-the-art approaches in terms of network lifespan, energy, and fairness.

Lightweight Secure Key Distribution Protocol (LSKDP) for Wireless Sensor Networks

Because of the enormous technological revolution, the most difficult tasks that humans cannot perform are now easily addressed by small digital gadgets. An example of this technological revolution is Wireless Sensor Networks (WSN). In the WSN, numerous heterogeneous types of sensors work together to sense, monitor, capture, process, and control the physical condition of a specific region or object. The WSN has been used extensively in a number of key areas, particularly in the medical, agricultural, military, and traffic management sectors. The little electronic gadget that forms the WSN is composed of three major components: a sensing component, a power storage component, and a wireless transmission component. Despite the numerous benefits of this modern technology, security remains an unresolved concern. The goal of this work is to develop an efficient Lightweight Secure Key Distribution Protocol (LSKDP) for enhancing the process's safety in heterogeneous medical WSNs. Additionally, security and performance analyses were conducted to demonstrate the suggested methodology's efficacy. The security analysis is performed to ascertain the proposed method's vulnerability to network threats. Additionally, performance analysis is used to determine the CPU utilization, memory consumption, processing power, and computational overhead of the suggested methodology. The experimental results indicate that the suggested strategy detects attacks more accurately and with a lower computational overhead.

An Individual Node Delay Based Efficient Power Aware Routing Protocol for Wireless Heterogeneous Sensor Networks

Miscellaneous node transmission ranges builds up Wireless Heterogeneous Sensor Networks (WHSNs). Designing an efficient, reliable and scalable routing protocol for WHSNs with intermittent asymmetric links is a challenging task. In this paper, we propose an efficient power aware routing scheme for WHSNs, which can provide loop-free, stateless, source-to-sink routing scheme without using prior information about neighbor. It uses both symmetric and asymmetric links to forward data from source to sink. The source node broadcasts location information to all its neighbor nodes. Each neighbor node calculates a delay slot based on the information obtained from the source to forward its power value to it. The node that has a minimum delay slot forwards the power earlier than the other nodes during contention phase and the delay slot is used to suppress the selection of unsuitable low-power nodes at that time. We also prove that our protocol is loop-free assuming no failures in greedy forwarding. By simulations we show that our protocol significantly outperforms the existing protocols in WHSNs.

An algorithm for enhancing coverage and network lifetime in cluster-based Wireless Sensor Networks

Majority of wireless sensor networks (WSNs) clustering protocols in literature have focused on extending network lifetime and little attention has been paid to the coverage preservation as one of the QoS requirements along with network lifetime. In this paper, an algorithm is proposed to be integrated with clustering protocols to improve network lifetime as well as preserve network coverage in heterogeneous wireless sensor networks (HWSNs) where sensor nodes can have different sensing radii and energy attributes. The proposed algorithm works in proactive way to preserve network coverage and extend network lifetime by efficiently leveraging mobility to optimize the average coverage rate using only the nodes that are already deployed in the network. Simulations are conducted to validate the proposed algorithm by showing improvement in network lifetime and enhanced full coverage time with less energy consumption