Enhanced Distributed Energy-Efficient Clustering (DEEC) Protocol for Wireless Sensor Networks: A Modular Implementation and Performance Analysis

Improving network lifetime is an important challenge in Heterogeneous Wireless sensor Networks. The best way to improve a heterogeneous WSN lifetime is to regrouping the network by the clusters which each one is managed by a leader called Cluster Head. The election of cluster head is based on a predefined probability that each node becomes cluster head and other nodes must join their Cluster Heads to send them their data. These cluster members can be closer to the base station and must send data to their manager, so there is a loss of energy in the network. In this paper, we propose a new technique to improve a threshold distributed energy efficient clustering protocol for heterogeneous wireless sensor networks by excluding the closer nodes to the base station in the clustering process. We demonstrate by simulation in MATLAB that the proposed approach increase the lifetime by around 46% and number of the received packet messages by around 184% than current heterogeneous WSN protocols as TDEEC.

Wireless sensor networks (WSNs) have enabled an improved awareness in a range of purposes like battlefield surveillance, disaster management, industrial process monitoring and control, machine health monitoring, and medical applications. Accordingly, great numbers of sensor nodes (SNs) are arranged for usages that can function as an individual. Because of minute power of batteries in WSNs, proficient exploitation of this power is a significant aspect. Clustering is a capable method to expand the lifespan of WSNs by diminishing the power utilization. In the research work, the authors have compared resourceful power consumption protocols using different variants of distributed energy-efficient clustering (DEEC), namely developed DEEC (DDEEC), enhanced DEEC (EDEEC), and threshold DEEC (TDEEC). The simulation results demonstrate that out of three variants of DEEC, TDEEC performs better than other two protocols in respect of stability and WSN lifespan.

In this paper, the optimization strategies of routing protocols are analysed with respect to energy utilization of sensor nodes in Wireless Sensor Networks (WSNs). Routing Protocols are in charge of discovering and maintaining the routes in the network. Different routing mechanisms have been proposed to address energy optimization problem in sensor nodes. Clustering mechanism is one of the most efficient mechanisms which cater to the requirements of energy conservation in wireless sensor networks. To check the efficiency of different clustering scheme against modelled constraints, we select five cluster based routing protocols; Low Energy Adaptive Clustering Hierarchy (LEACH), Threshold Sensitive Energy Efficient sensor Network (TEEN), Stable Election Protocol (SEP), Distributed Energy Efficient Clustering (DEEC), and Hybrid Energy Efficient Distributed protocol. To validate our mathematical framework, we perform analytical simulations in MATLAB by choosing number of alive nodes, number of dead nodes, number of packets, number of cluster heads, as performance metrics.

Energy efficiency has become a primary issue in wireless sensor networks (WSN). The sensor networks are powered by battery and thus they turn out to be dead after a particular interval. Hence, enhancing the data dissipation in energy efficient manner remains to be more challenging for increasing the life span of sensor devices. It has been already proved that the clustering method could improve or enhance the life span of WSNs. In the clustering model, the selection of cluster head (CH) in each cluster regards as the capable method for energy efficient routing, which minimizes the transmission delay in WSN. However, the main problem dealt with the selection of optimal CH that makes the network service prompt. Till now, more research works have been processing on solving this issue by considering different constraints. Under this scenario, this paper attempts to develop a new clustering model with optimal cluster head selection by considering four major criteria like energy, delay, distance, and security. Further, for selecting the optimal CHs, this paper proposes a new hybrid algorithm that hybridizes the concept of dragon fly and firefly algorithm algorithms, termed fire fly replaced position update in dragonfly. Finally, the performance of the proposed work is carried out by comparing with other conventional models in terms of number of alive nodes, network energy, delay and risk probability.

Classical clustering protocols in wireless sensor networks (WSNs) assume that all nodes are equipped with the same amount of energy. As a result, they cannot take full advantage of the presence of node heterogeneity. In this study, a stable and energy‐efficient clustering (SEEC) protocol for heterogeneous WSNs is proposed. In addition, the extension to multi‐level of SEEC is presented. It depends on the network structure that is divided into clusters. Each cluster has a powerful advanced node and some normal nodes deployed randomly in this cluster. In the multi‐level architectures, more powerful supper nodes are assigned to cover distant sensing areas. Each type of nodes has its role in the sensing, aggregation or transmission to the base station. At each level of heterogeneity, the optimum number of powerful nodes that achieves the minimum energy consumption of the network is obtained. The proposed protocol is a heterogeneous awareness to prolong the stability period, which is crucial for many applications. The performance of the proposed protocol is compared by existing homogeneous and heterogeneous protocols. Simulation results show that the proposed protocol provides a longer stability period, more energy efficiency and higher average throughput than the existing protocols.

Due to the restricted hardware resources of the sensor nodes, modelling and designing energy efficient routing methods to increase the overall network lifetime have become one of the most significant strategies in wireless sensor networks (WSNs). Cluster‐based heterogeneous routing protocols, a popular part of routing technology, have proven effective in management of topology, energy consumption, data collection or fusion, reliability, or stability in a distributed sensor network. In this article, an energy efficient three‐level heterogeneous clustering method (DEEC) based distributed energy efficient clustering protocol named TBSDEEC (Threshold balanced sampled DEEC) is proposed. Contrary to most other studies, this study considers the effect of the threshold balanced sampled in the energy consumption model. Our model is compared with the DEEC, EDEEC (Enhanced Distributed Energy Efficient Clustering Protocol), and EDDEEC (Enhanced Developed Distributed Energy Efficient Clustering Protocol) using MATLAB as two different scenarios based on quality metrics, including living nodes on the network, network efficiency, energy consumption, number of packets received by base station (BS), and average latency. After, our new method is compared with artificial bee colony optimization (ABCO) algorithm and energy harvesting WSN (EH‐WSN) clustering method. Simulation results demonstrate that the proposed model is more efficient than the other protocols and significantly increases the sensor network lifetime.

Wireless sensor networks congestion occurs easily due to its centralized traffic pattern. Normally, mono-sync wireless sensor network experiences multiple traffic flow congestion in the dense environment of the network, which leads to excess energy consumption and severe packet loss. To overcome these issues, a congestion detection and alleviation mechanism using cluster based heuristic optimized hierarchical routing protocol is proposed in this paper. Here, congestion detection and alleviation utilize the features of sensor nodes. The congestion is categorized into two types: (i) node level congestion and (ii) link level congestion. The node level congestion is detected by assessing the buffer utilization and the interval amid the consecutive data packets. The link level congestion is considered with computing link usage utilizing back-off step of round robin carrier sense multi-access with collision avoidance. Congestion detection and alleviation reactively affected node/link through cuckoo hosted rider search multi-hop routing algorithm. It has two phases: the cluster head selection and multi-path routing. Cluster head selection is performed through Taylor multiple random forest kernel fuzzy C-means clustering algorithm and multi-path routing is performed through cuckoo hosted rider search multi-hop routing algorithm. The simulation of the proposed method is done in network simulator tool. Here, the performance metrics, like packet delivery ratio, delay, energy consumption, packet drop, overhead, network lifetime and throughput are calculated. The experimental outcomes of the proposed technique shows 11.6%, 18.4% and 28.1% lower delay, 78.2%, 65.4% and 52.6% higher packet delivery ratio, and 29.2%, 37.4% and 40.8% lower packet drop compared with the existing methods, like congestion detection and alleviation using multi-attribute decision-making in optimization-based hybrid congestion alleviation routing protocol in wireless sensor networks, congestion detection and alleviation using hybrid K-means with greedy best first search algorithms in packet rate reduction utilizing adaptive weight firefly algorithm ant colony optimization based routing protocol in wireless sensor networks and congestion detection and alleviation using multi-input time on task optimization algorithm for altered gravitational search algorithm routing protocol in wireless sensor networks.

The application-oriented Internet of Things (IoT) systems that exhibit the use of wireless sensor networks (WSNs) have energy constraint issues. The nodes in the WSN are driven by batteries that cannot be used for a very long time and thus the network is unable to combat the energy efficiency challenge. Also, the energy of the nodes drains rapidly with time as a result of a steady sensing task. Moreover, there are several intermediate tasks performed by the wireless network from sensing to sending the data to the destination. The traditional wireless models can accomplish the task of sensing and transmitting but are unable to avoid the tradeoff between many quality-of-service matrices such as network latency and throughput. So, there is a need to employ optimization techniques with a multi-objective paradigm. In this paper, a model for both choosing the cluster head and selecting the efficient path in a WSN for IoT applications has been proposed. The cluster head selection which is a part of clustering is done using a multi-objective rider optimization algorithm (ROA) which considers 3 objectives namely energy, distance, and delay. The routing is performed by selecting efficient and optimal paths using the multi-objective sailfish optimization algorithm (SFO). The results reveal that the proposed model proves itself superior to other similar existing works when compared based on execution time, energy depletion, network delay, throughput, packet delivery ratio, alive nodes in the network, and increase in dead nodes. The experimentation is done on a dense sensor network and it is observed that the proposed work can mitigate up to 30-40% of energy utilization and 40-60% of delay when compared with similar multi-objective techniques for routing and clustering. The intensification in the network lifespan and throughput is also marked by the proposed multi-objective technique which makes it profitable to be used in various IoT applications.

Wireless sensor networks are a promising application of the Internet of Things in the sustainable development of smart cities, and have been afforded significant attention since first being proposed. Authentication protocols aim to protect the security and confidentiality of legitimate users when accessing and transmitting data. However, existing protocols may suffer from one or more security flaws. Recently, Butt et al. proposed an energy-efficient three-factor authentication protocol for wireless sensor networks. However, their protocol is vulnerable to several attacks, and lacks certain security properties. In this paper, the causes of these design flaws are analyzed. Furthermore, we propose a novel three-factor authentication protocol (password, smart card, and biometric information) for wireless sensor networks in Internet of Things contexts. A dynamic anonymous strategy is designed to prevent privacy disclosure and to resist sensor node capture attacks, tracking attacks, and desynchronization attacks. The Find–Guess model and random oracle model are combined to prove the security of the proposed protocol. A comparative analysis with related schemes shows that the proposed protocol has higher security and is able to maintain a low computational overhead.

In wireless sensor networks, there has been a huge number of sensor nodes and all are handled with limited potential supplied by the battery source. The nodes in the network use energy to sense and process the information collected from the environment and communicate it to the Base Station. It is assigned with the problem to decrease the energy consumption, a number of routing and clustering protocols have been proposed so as to get large energy efficiency in heterogeneous network structure. Clustering plays a significant and important role to control the energy consumption at the nodes, as the routing and transfer of data. It is done by the cluster heads and hence prolongs the network lifetime. we proposed dispense and energy conscious clustering protocols that follow the thoughts of Distributed Energy Efficient Clustering protocol which produces a better results as compared to DCHRP, ETSSEP, TSEP and SEP.

Nowadays Wireless Sensor Networks WSNs are playing a vital role in several application areas ranging health to battle field. Wireless sensor networks are easy to deploy due to its unique characteristics of size and self-organizing networks. Wireless sensor nodes contain small unchangeable and not chargeable batteries. It is a resource constraint type network. Routing in WSN is most expensive task as it utilizes more power resources. This paper is intended to introduce energy efficient routing protocol, known as Position Responsive Routing Protocol (PRRP) to enhance energy efficiency of WSN. Position responsive routing protocol differs in several ways than other existing routing techniques. Position response routing protocol approach allows fair distribution of gateway\cluster head selection, maximum possible distance minimization among nodes and gateways\cluster heads to utilize less energy. Position responsive routing protocol shows significant improvement of 45% in energy efficiency of wireless sensor network life time as a whole by increasing battery life of individual nodes. Furthermore PRRP shows drastic increases for data throughput and provide better solution to routing energy hole due to it fair distributed approach of gateway selection. This work is the extension of Energy efficient routing protocol for wireless sensor network published in IEEE ICACT 2014.

The Internet of Things (IoT) connects physical and digital systems, enabling smart devices to continuously generate data through embedded sensors. However, ensuring high data quality in an energy‐constrained environment remains a key challenge. Wireless sensor networks (WSNs), central to IoT communication, often struggle with efficient data handling due to limited energy resources and large data volumes. This paper proposes FiQoT (Fog‐Integrated Quality‐Oriented IoT Architecture), which integrates fog computing with WSNs to enhance both energy efficiency and data quality. By introducing a fog layer between the sensor and cloud layers, data is processed closer to the source, reducing communication overhead and improving responsiveness. At its core, FiQoT uses an energy‐efficient distance‐based (EEDB) clustering algorithm designed to improve cluster head selection and balance energy consumption across nodes. FiQoT adopts a multi‐sink approach for load balancing and incorporates essential preprocessing to ensure more reliable data. A user authorization mechanism in the IoT module further strengthens data security, enabling remote access and visualization of real‐time data. To emulate real‐world scenarios, an open‐source IoT temperature dataset has been used to test the system's robustness. MATLAB simulations show that FiQoT outperforms HEED, LEACH, and I‐SEMP clustering algorithms in terms of energy consumption, network lifetime, and cluster head stability. These results highlight FiQoT's potential as a practical and scalable solution for next‐generation IoT applications.

SummaryIn the past few years, restricted wireless sensor networks (WSNs) enabled the Internet of Things (IoT) have attracted significant attention and expansion to enhance service delivery and resource efficiency. Dissemination is a service offered by WSN that uses radio transmission and over‐the‐air programming for updating the deployed sensor nodes through online. The centralized data dissemination methods are replaced by the distributed approaches because they affect the drawbacks of a single point of failure, no scalability, and insecurity. Therefore, an Energy Efficient Protocol for Data Dissemination in Wireless Sensor network‐enabled IoT using Domain‐Adaptive Message Passing Graph Neural Network (EEP‐WSN‐IoT‐DMPGNN) is proposed in this paper. The nodes are formed as clusters utilizing the Deep Fuzzy Curriculum Clustering (DFCC) technique that rewards nodes belonging to a given cluster. By using the Crayfish Optimization Algorithm (COA), the Cluster Head (CH) selection optimally chose the ideal CH and satisfies the multiple objective functions, such as energy, delay, traffic density, and distance. Afterward, domain‐adaptive Message Passing Graph Neural Network (DMPGNN) based routing protocol is developed, the input given to the routing protocol includes a sink, action history, future node, and maximum‐distance node, which attains enhanced data transfer in the chosen path. The proposed technique attains a lower no. of dead nodes, lower energy consumption, and higher Network Lifetime while analyzed with existing techniques, such as routing technique depending on deep learning for effectual data transmission in 5G WSN communication (DL‐RPDT‐WSN), Reinforcement‐Learning base energy effectual optimized routing protocol in WSN (RL‐EERP‐WSN), and Energy‐efficient intellectual routing method for IoT‐enabled WSN (EIR‐IoT‐WSN), respectively.

In wireless sensor networks (WSNs), the protocols for routing have a great effect in performance of network as network’s lifetime, good energy organization, etc. These protocols are developed based on the different schemes like clustering, chaining and cost based. The WSN contains a large number of nodes which are sometimes difficult to manage. So, the best way is to make a cluster by combining various nodes, and this technique is known as clustering. By doing so, the energy exhaustion by nodes can be restricted. A node is nominated as cluster head (CH) to handle the communication amongst nodes and managing of nodes in the cluster. An ACO-based probability rule is used for choosing the CH amid the cluster nodes. The data are sent from cluster nodes to the CH; then, it further sends the relevant details to the base station (BS). ACO-DEEC: ant colony optimization-based distributed energy efficient clustering protocol is used for probability rule calculation for CH selection depending on the metrics, i.e. efficiency of the nodes and distance amongst nodes. The algorithm proposed enhances the parameters like energy consumption by cluster nodes, finding dead nodes in cluster and quantity of packets sent to the BS as compared with existing DEEC protocol.KeywordsACO-DEECAnt Colony optimizationCluster headDEEC protocolWireless sensor network

The Quality of Service (QoS) and energy efficiency are the key issues for Future Internet. Targeting multiple objectives for efficient routing is a critical and demanding task in recent applications. Due to increase in mobility applications consideration of mobility parameters for dynamic routing is necessary. The objective of this research paper is to propose the multi-objective approach for dynamic routing in Wireless Multimedia Sensor Network. Extending the adaptive routing for mobility will definitely enhance the capabilities of routing protocols for Wireless Multimedia Sensor Network. In this paper, we propose the adaptive routing protocol which is considering mobility parameter during route establishment. We are using various parameters such as Remaining Energy, Hop count, Link Quality Index and Mobility Factor for path formation. The experimentation results show that mobility parameter extension improves network performance. The technique used in this paper will enrich applications for Internet of things (IoT).