Homogeneous Wireless Sensor Networks Research Articles

Retraction Note: Quality analysis of multi-sensor intrusion detection node deployment in homogeneous wireless sensor networks

Retraction Note: Quality analysis of multi-sensor intrusion detection node deployment in homogeneous wireless sensor networks

Optimal Stable Cluster Head Selection Method for Maximal Throughput and Lifetime of Homogeneous Wireless Sensor Network

Optimal Stable Cluster Head Selection Method for Maximal Throughput and Lifetime of Homogeneous Wireless Sensor Network

A dynamic and multi-level key management method in wireless sensor networks (WSNs)

Wireless sensor networks include a set of ultralight sensor nodes with limited energy, low storage capacity, and constrained processing power. Security is a challenging issue in these networks. One approach to maintain security in these networks is key management. When designing key management schemes, the main challenge is to achieve the acceptable security level and manage resources, especially energy. Today, many key management methods use authentication mechanisms. However, these methods are faced with different challenges due to the limited computational capability of nodes and high energy consumption in the network. In this paper, we present a dynamic and multi-level key management method for homogeneous wireless sensor networks. The proposed key management approach uses an authentication mechanism based on message authentication code (MAC), which is calculated by the shared symmetric keys between the transmitter and receiver nodes. In our scheme, the network is divided into five levels. Then, the clustering process is performed at each level, so that the size of the clusters varies in different levels. The proposed approach includes four phases: (1) Network leveling phase (2) Clustering phase (3) Key establishment phase (cluster key, pairwise key, and gateway key) (4) Rekeying phase. In the proposed method, a symmetric encryption algorithm called RC5 is used for producing keys. Our method contributes to the state of the art in wireless sensor networks by proposing a dynamic and multi-level key management approach. The use of a MAC-based authentication mechanism and the division of the network into different levels and clusters allows for efficient key establishment and rekeying, while minimizing energy consumption and communication overhead. Our proposed method is implemented using the NS2 simulator. Then, the simulation results are compared with SKWN and KMP methods. The results show that our proposed method outperforms SKWN and KMP in terms of average energy consumption, required memory, and communication overhead. However, its computational overhead is slightly more than SKWN and KMP.

An Innovative Clustering Hierarchical Protocol for Data Collection from Remote Wireless Sensor Networks Based Internet of Things Applications

Recently, unmanned aerial vehicles (UAVs) have emerged as a viable solution for data collection from remote Internet of Things (IoT) applications. However, the successful implementation in this regard necessitates the development of a reliable and energy-efficient routing protocol. This paper proposes a reliable and an energy-efficient UAV-assisted clustering hierarchical (EEUCH) protocol designed for remote wireless sensor networks (WSNs) based IoT applications. The proposed EEUCH routing protocol facilitates UAVs to collect data from ground sensor nodes (SNs) that are equipped with wake-up radios (WuRs) and deployed remotely from the base station (BS) in the field of interest (FoI). During each round of the EEUCH protocol, the UAVs arrive at the predefined hovering positions at the FoI, perform clear channel assignment, and broadcast wake-up calls (WuCs) to the SNs. Upon receiving the WuCs by the SNs’ wake-up receivers, the SNs perform carrier sense multiple access/collision avoidance before sending joining requests to ensure reliability and cluster-memberships with the particular UAV whose WuC is received. The cluster-member SNs turn on their main radios (MRs) for data packet transmission. The UAV assigns time division multiple access (TDMA) slots to each of its cluster-member SNs whose joining request is received. Each SN must send the data packets in its assigned TDMA slot. When data packets are successfully received by the UAV, it sends acknowledgments to the SNs, after which the SNs turn off their MRs, completing a single round of the protocol. The proposed EEUCH routing protocol with WuR eliminates the issue of cluster overlapping, improves the overall performance, and increases network stability time by a factor of 8.7. It also improves energy efficiency by a factor of 12.55, resulting in a longer network lifespan compared to Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. Moreover, EEUCH collects 5.05 times more data from the FoI than LEACH. These results are based on simulations in which the EEUCH protocol outperformed the existing six benchmark routing protocols proposed for homogeneous, two-tier, and three-tier heterogeneous WSNs.

NMSFRA: Heterogeneous routing protocol for balanced energy consumption in mobile wireless sensor network

Wireless sensor networks are widely used in various fields and consist of a large number of sensor nodes that are powered by batteries. As a result, energy efficiency is crucial for the effective operation of these networks. With the advancement of cross technology communication (CTC), the presence of heterogeneous wireless nodes, especially mobile nodes, in wireless sensor networks has increased, making traditional routing protocols for homogeneous wireless sensor networks inapplicable. To overcome the above issues, this paper proposes a heterogeneous wireless sensor network routing protocol for a balanced energy consumption, NMSFRA (NGO based mean shift fuzzy clustering routing algorithm). In order to mitigate the impact of uneven cluster distribution and unstable network links due to the mobility of nodes, the proposed protocol first divides the network into balanced clusters using the Mean Shift (MS) method. The Mamdani fuzzy inference system is then used to take node mobility into account. The protocol dynamically selects different parameters as inputs to the fuzzy logic, enabling it to choose cluster heads (CHs) based on the overall location of the clusters. Additionally, to extend the network lifetime, the protocol generates multi-hop forwarding paths using the Northern Goshawk Optimization (NGO) method. Finally, the paper presents experimental simulations carried out on three different network topologies and compares the results with existing protocols such as LEACH, EETPF, E-FUCA, and DE-SEP. The results show that the NMSFRA protocol extends the survival period of nodes by 235 rounds, 240 rounds, 364 rounds, and 190 rounds, on average, compared to the LEACH, EETPF, E-FUCA, and DE-SEP protocols, respectively. The network throughput also increased by 154.92%, 48.74%, 84.77%, and 106.77%, respectively. The NMSFRA protocol also showed better performance in terms of total residual energy, FND, HND, and average speed difference between nodes in the first 5 rounds. Therefore, the proposed NMSFRA protocol demonstrates superior performance in terms of network lifetime, energy utilization, throughput, and network stability.

Optimal vs rotation heuristics in the role of cluster-head for routing in IoT constrained devices

Wireless Sensor Networks (WSNs) are crucial for implementing the Internet of Things (IoT) vision. A WSN can include distributed devices that can be battery powered, have a small amount of memory, have limited CPU processing capabilities and limited transmission range. WSNs are categorised into two main types that are homogeneous and heterogeneous. Homogeneous WSNs are composed of nodes that are all equal while heterogeneous ones are composed of nodes that can have different transmission rate and different initial energy. Collecting data in an energy efficient manner is one of the main goals when building wireless sensor networks since devices are battery powered and battery replacement can be difficult or impossible to be performed. Clustering protocols are one of the main approaches that have been used in order to collect data in an energy efficient way. A clustering protocol groups nodes into a set of clusters. Each cluster has a representative node that is referred to as Cluster Head (CH). This collects data from its cluster members and forwards them to an external Base Station, possibly in a multi-hop way among cluster heads. A rotation strategy for electing the cluster head is often used together with clustering in order to prolong the network lifetime. Rotation eliminates the overhead traffic that is needed for leader election and cluster formation since the old cluster heads directly designate new ones. In this work, we describe an Optimum Rotation Scheduling (ORS) that uses Integer Linear Programming in order to find the optimum rotation strategy. ORS assumes the WSN clusters are already formed by using some clustering protocol. A novel integer linear programming formulation is then used in order to define a cluster head rotation that produces the optimum cluster lifetime. Comparisons with existing heuristics and the provided optimum are then shown and discussed by means of extended simulations involving both homogeneous and heterogeneous WSNs.

OPTIMIZATION OF CLUSTER HEAD SELECTION AND ENHANCING ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORKS USING NOVEL METAHEURISTIC ALGORITHMS

Clustering-based routing is preferred to support fault tolerance, load balancing, reliable communication, and to prolong the network lifetime in a wireless sensor network (WSN). The low-energy adaptive clustering hierarchy (LEACH) is the most popular routing technique, introduced for the first time for homogeneous WSNs. However, the random selection of cluster heads (CHs) in LEACH protocols results in poor performance in real network deployments due to the faster rate of energy depletion at CHs. The dynamic selection of CHs based on a heuristic approach can minimize the energy consumption at CHs and enhance the network lifetime. In this paper, a metaheuristic algorithm called grey wolf optimization (GWO) and its enhanced versions are proposed in selecting the optimal CH. The fitness function is defined based on sink distance to CH and residual energy at the sensor node. The optimal values of fitness function give an efficient CH selection and cost-effective routing. The primary goal of this paper is to maximize the network lifetime of WSNs by optimal selection of CHs using the improved GWO (IGWO) algorithm. The proposed IGWO-based LEACH protocol confirmed the optimal selection of CH with minimum energy consumption, resolved premature convergence, and enhanced the network lifetime by balancing the number of alive and dead nodes in WSN.

Energy efficient with prolonging lifetime in homogeneous wireless sensor networks

In this artical, we suggest a new clustering model called the Fuzzy Low Energy Adaptive Clustering Hierarchy Protocol (FLEACH). It has the ability to go around obstacles and fix the issue of uneven energy consumption in homogenous wireless sensor networks (WSNs). In order to determine the best routing path for the homogeneous WSNs, we also suggest the Ant Colony Routing Method (ACORM), which is an energy-efficient routing technique. ACORM aims to investigate issues relating to balancing energy consumption and maximizing network life. To illustrate FLEACH-ACORM's effectiveness in managing energy consumption and optimizing homogeneous network life, we compare our system with two approaches: Power-Efficient Sensor Information Systems (PEGASIS) and LEACH. Results of the simulation indicate that the network lifetime achieved by FLEACH-ACORM could be increased by almost 28.5 % and 19.8 % more than that obtained by LEACHPEGASIS, LEACH clustering protocols, respectively.

Effective proactive routing protocol using smart nodes system

Small Power Restricted Unit (PRU) platform known as the Wireless Sensor Network (WSN) to monitor a Large Region of Interest (ROI) and send data to the Base Station (BS). Accurately capturing the ROI and communicating observed information to the BS over the longest period is indeed the main problem facing WSN. Despite the latest introduction of many power routing algorithms in regular monitoring applications, the variable environment and complex environment for WSN applications end up creating these procedures as an important task. This study Degree Restricted Tree (DRE) nodes for such networks, including a BS outside of the ROI in a homogeneous pre-emptive WSN. The optimal degree of a node with low DRT energy consumption is determined because the degree of a node affects the network lifespan of these forms of connections. To provide an equitable distribution of the burden in terms of transmission power, this study then suggests a Joint Decentralized Antenna (JDA) algorithm which is based on several antenna theories. With an optimum node density and DRT base, JDA is made for frequent surveillance systems with real-time applications. The results validate our research, which emphasizes that the network throughput of DRT is doubled when utilizing optimum node angles as opposed to certain other node degrees. Additionally, it has been demonstrated that introducing JDA into DRT with ideal network density increases the network's latency thus eliminating the proportion between the unstable period and the lifetime of the network in halves. Additionally, it displays a 25% improvement in network lifespan and the lowest rate of node loss when compared to the existing system ensuring that halves of nodes are still alive just a few rounds even before the lifetime of the network expires.

An energy‐efficient clustering algorithm combined interval type‐2 fuzzy logic and dual‐super‐cluster‐head mechanism for homogeneous wireless sensor networks of mobile sink

SummaryIn recent trends, wireless sensor network (WSN) is often used in harsh environments. When WSNs work, they need to deploy a large number of sensor nodes in the monitoring area. Due to the limited resources carried by the sensor nodes, the monitoring process of WSN will be limited by the energy of the sensor nodes. An energy‐efficient clustering algorithm combining interval type‐2 fuzzy logic and dual‐super‐cluster‐head (IT2FL‐DSCH) mechanism is proposed, aiming at reducing and balancing the energy consumption of sensor nodes. Considering the four fuzzy parameters, that is, cluster heads' (CHs') battery power, CHs' centrality, base station's (BS's) mobility, and the distance from CHs to BS, interval type‐2 fuzzy logic (IT2FL) is used to select the appropriate super cluster head (SCH). And SCH is the only way to communicate with the mobile BS, thus effectively using bandwidth. Then, the DSCH mechanism to balance the energy consumption of SCH is introduced in detail. In addition, we utilize mathematical derivation to obtain the supremum of the energy consumption of the SCH algorithm under different network distribution. Finally, the MATLAB simulation results show that this protocol outperforms low energy adaptive clustering hierarchy (LEACH), LEACH‐centralized (LEACH‐C), distributed energy efficient clustering (DEEC), WSNs using deep‐Q‐networks (DQN), and two‐tier distributed fuzzy logic based protocol (TTDFP) in terms of sensor nodes energy saving, network stability, and network lifetime.

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.

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.

ADV-LEACH2 Energy-Efficient Clustering Protocols for Three-Tier Heterogeneous Wireless Sensor Networks

This paper presents energy-efficient clustering protocols for three-tier heterogeneous wireless sensor networks (WSNs) along with their applications to the systems. The performance of a new dynamic, modified, probability-based approach to finding the most energy-efficient cluster head nodes is intended to extend network lifetime. Mathematical modeling and simulation results from using MATLAB-2017b provide a comparative analysis of several heterogeneous and homogeneous WSN variants in terms of energy consumption, active versus dead nodes, and network lifetime based on first node dead (FND) and last node dead (LND). ADV-LEACH2 in a three-tier heterogeneous network performs better than homogeneous and two-tier heterogeneous networks owing to super nodes with higher energy levels.

A two‐vectordata‐predictionmodel forenergy‐efficient data‐aggregationin wireless sensor network

AbstractMost ecological management applications use wireless sensor networks (WSNs) to collect data regularly, with great temporal redundancy. As a result, a significant amount of energy is used transmitting redundant data, making it tremendously problematic to attain a satisfactory network lifetime, which is a bottleneck in enduring such environmental monitoring applications. A two‐vector data prediction model that is based on normalized quantile regression (NQR) is proposed to proficiently accomplish energy reduction in synchronous data collecting cycles. The introduced NQR algorithm provides high‐accuracy data prediction. With accurate estimates and reduced data transmission, energy usage is reduced. Furthermore, it extends the network's lifetime. In intracluster transmissions, NQR uses a two‐vector data‐prediction algorithm to coordinate the estimated sensor's reading, and, as a result, it will minimize cumulative inefficiencies from uninterrupted predictions. NQR algorithm can be integrated with both homogeneous and heterogeneous WSNs. When compared to state‐of‐art methods, the suggested NQR methodology is shown to have high energy efficiency, greater prediction accuracy, and more positive predictions with high data quality, which help the network to last longer.

An Improved Gateway-Based Energy-Aware Multi-Hop Routing Protocol for Enhancing Lifetime and Throughput in Heterogeneous WSNs

In this work, an improved version of the gateway based multi-hop routing protocol was studied. The MGEAR protocol is mainly used for prolonging network lifetime in homogeneous wireless sensor network. Herein, the proposed approach aims to prolong network lifetime and enhance the throughput of this protocol in the case of heterogeneous wireless sensor networks (HWSNs). In the MGEAR, the network is divided into several fields; sensor nodes in the first field communicate directly with the base station. Sensors in the center of the network send their data to the gateway which perform data fusion and spread to the base station. The rest of nodes are divided into two equal regions, in each region sensor nodes are grouped into clusters with a leading node as cluster-head. The central point of our approach is the selection of cluster-heads which is based on a ratio between the residual energy of each sensor node and the average energy of the region which it belongs. In order to equalize the load and prolong the lifetime of sensors, the cluster-head election probability is computed in each round according to the residual energy of each sensor node. Finally, the simulation results showed that this model had higher throughput and increased the lifetime by 130%, 151%, 167%, 171%, and 215% compared to HCR, ERP, ModLEACH, D-MSEP and DDEEC protocols respectively in the case of 2-level heterogeneity. In case of 3-level heterogeneity, the network lifetime is increased by 123%, 150%, 163% and 218% compared to HCP, ModLEACH, hetSEP and hetDEEC.

Improved bees algorithm for the deployment of homogeneous and heterogeneous wireless sensor networks

Nowadays, countless applications benefit from the services provided by wireless sensor networks (WSNs) in remote surveillance and data gathering. Coverage is considered an important quality of service (QoS) criterion for many of these applications. To ensure that the required QoS is achieved, the sensors must be placed in locations that optimise the area coverage and eliminate the coverage holes, especially when deploying sensors with heterogeneous sensing ranges. In this paper, a novel sensor deployment scheme based on the improved bees algorithm (IBA) for optimising the area coverage in both homogeneous and heterogeneous WSNs is proposed. The IBA includes a neighbourhood shrinking procedure that aims to improve the local search efficiency and a site abandonment procedure for preventing the algorithm from the entrapment in a local optimum. Experiments show that the IBA delivers high-quality solutions and optimises the area coverage effectively compared with four state-of-the-art methods in homogeneous and heterogeneous WSNs.

Improved bees algorithm for the deployment of homogeneous and heterogeneous wireless sensor networks

Improved bees algorithm for the deployment of homogeneous and heterogeneous wireless sensor networks

Towards Hybrid Energy-Efficient Power Management in Wireless Sensor Networks.

Wireless Sensor Networks (WSNs) are prone to highly constrained resources, as a result ensuring the proper functioning of the network is a requirement. Therefore, an effective WSN management system has to be integrated for the network efficiency. Our objective is to model, design, and propose a homogeneous WSN hybrid architecture. This work features a dedicated power utilization optimization strategy specifically for WSNs application. It is entitled Hybrid Energy-Efficient Power manager Scheduling (HEEPS). The pillars of this strategy are based on the one hand on time-out Dynamic Power Management (DPM) Intertask and on the other hand on Dynamic Voltage and Frequency Scaling (DVFS). All tasks are scheduled under Global Earliest Deadline First (GEDF) with new scheduling tests to overcome the Dhall effect. To minimize the energy consumption, the HEEPS predicts, defines and models the behavior adapted to each sensor node, as well as the associated energy management mechanism. HEEPS’s performance evaluation and analysis are performed using the STORM simulator. A comparison to the results obtained with the various state of the art approaches is presented. Results show that the power manager proposed effectively schedules tasks to use dynamically the available energy estimated gain up to 50%.

Connectivity and coverage constrained wireless sensor nodes deployment using steepest descent and genetic algorithms

Connectivity and different coverage types including target, area, and barrier coverages are among the most critical challenges of wireless sensor networks (WSN). This paper investigates node deployment for the challenges of target coverage, area coverage, and connectivity over randomly distributed homogeneous and heterogeneous WSNs, and suggests a new method in both centralized and distributed modes which (i) covers all targets by the required number of sensors, (ii) provides maximum area coverage and connectivity over the network, and (iii) manages sensors' movement from the initial locations to the final ones. Specifically, in the case of target coverage, a new analytical deployment method using the steepest descend (SD) algorithm with Armijo and Wolf rules is proposed instead of evolutionary methods. The results demonstrate this method outperforms over Genetic algorithm (GA) in managing sensors' movement towards targets, guaranteeing 100% coverage in targets, and providing a significant reduction- more than 40% in the worst condition-in algorithm complexity. However, when area coverage and connectivity are also considered, a new hybrid deployment method, which first uses GA to extract the optimal sensors' coordinates via analyzing these challenges simultaneously and then employs SD algorithm to move sensors towards these locations, is suggested. Numerical results confirm the complexity of this two-step method is approximately identical to the GA method of existing research; however, it provides sensors' trajectory and more accuracy for network coverage and connectivity.

Adaptive cooperative routing transmission for energy heterogeneous wireless sensor networks

The mass machine type communication (mMTC) in 5G requires the technical support of the wireless sensor network (WSN). Compared with homogeneous WSNs, heterogeneous WSNs provide more flexibility and are more in line with the reality. For heterogeneous WSNs, the use of proper routing methods can prolong the lifetime as well as the stability of the system. This paper studies an energy heterogeneous WSN, where nodes can cooperate with each other to improve the energy efficient of the network. We focus on the network routing within distributed energy-efficient clustering (DEEC) protocol, a typical networking/communication protocol for heterogeneous WSNs. First, we derive a proposition on the choice of cooperative single-hop or multi-hop modes, which gives a sufficient condition on cooperative multi-hop transmission. Furthermore, we present two lemmas, which are the conditions on the distance threshold for mode selection. Based on the above, a novel adaptive cooperative routing algorithm combined with DEEC is proposed. Simulation results show that the proposed routing algorithm outperforms the benchmark schemes and can effectively reduce the network energy consumption and prolong the network lifetime.