RaCH: A New Radial Cluster Head Selection Algorithm for Wireless Sensor Networks

Energy conservation is the most significant issue in wireless sensor networks due to limited power sources of sensor nodes that cannot be recharged or replaced. Clustering is considered to be energy-efficient in WSN as it reduces the number of sensor nodes taking part in long range communication. During clustering, each sensor node selects a cluster head (CH) to join a cluster among several candidate CHs. The selection of cluster head uses different parameters such as residual energy of cluster head and distance between node and cluster head. A poor selection of cluster head will lead to increased energy consumption in network that will degrade the network lifetime. Furthermore, sensor nodes are likely to fail due to different factors such as limited energy and environment hazards. So, the fault tolerance is another challenge for long run of the WSNs. In this paper, we propose a distributed fault-tolerant multi-objective clustering algorithm (DFMCA). In this algorithm, each sensor takes its decision using local information only. We also propose a recovery algorithm in case of sudden death of the cluster heads in the network. A simulation result demonstrates that our algorithm outperforms the existing state-of-the-art algorithms, namely FTCA and LBCA, in terms of network lifetime.

The rapid growth in wireless technology is enabling a variety of advances in wireless sensor networks (WSNs). By providing the sensing capabilities and efficient wireless communication, WSNs are becoming important factor in day to day life. WSNs have many commercial, industrial and telecommunication applications. Maximizing network lifespan is a primary objective in wireless sensor networks as the sensor nodes are powered by a non-rechargeable battery. The main challenges in wireless sensor networks (WSNs) are area of coverage, network’s lifetime and aggregating. Balanced node establishment (clustering) is the foremost strategy for extending the entire network's lifetime by aggregating the sensed information at the head of the cluster. The recent research trend suggests Meta-heuristic algorithms for the intelligent selection of ideal Cluster Heads (CHs). The existing Cluster Head Selection (CHS) algorithm suffers from the inconsistent trade-offs between exploration – exploitation and global best examine constraints. In this research, a novel Camel series Elephant Herding Optimization (CSEHO) algorithm is proposed to enhance the random occurrences of Camel algorithm by the Elephant Herding Optimization algorithm for optimal CHS. The Camel algorithm imitates the itinerant actions of a camel in the desert for the scavenging procedure. The visibility monitoring condition of the camel algorithm improves the efficiency of exploitation, whereas the exploration inefficiency of a Camel algorithm is compensated optimally by the Elephant Herding Optimization operator (Clan and separator). The superior performance of the proposed CSEHO algorithm is validated by comparing its performance with various other existing CHS algorithms. The overall attainment of the offered CSEHO algorithm is 21.01%, 31.21%, 44.08%, 67.51%, and 85.66%, better than that of EHO, CA, PSO, LEACH, and DT, respectively.

Wireless sensor networks (WSNs) have been an emerging field in recent years. WSNs have major advantages in battlefields and emergency situations. However, due to the limited power of the sensor nodes, WSNs are restricted with their applications. The effective management of energy over the sensor nodes leads to good performance of the network. In this paper, we are proposing the energy-aware cluster head selection algorithm (ECHSA) for WSNs. The cluster head selection plays a major role in reducing the energy consumption of the cluster. To support our approach, the minimum energy transmission is proposed, and the simulation of the ECHSA is carried out with the NS-2 simulation. The results are compared with the sectored low energy adaptive cluster head (S-LEACH) and Theins algorithms. The results proved the efficiency of the proposed algorithm.

In general, environment monitoring cluster based hierarchical routing protocol is among the most common protocol being opted due to the load balancing among each other sensor. Sensors are randomly deployed in a specific area to collect useful information periodically for a few months or even a few years. Therefore, battery power limitation becomes a challenging issue. It is also impractical to maintain the network lifetime by changing the battery frequently. Low energy adaptive cluster hierarchical (LEACH) is one of the common clustering protocols that will elect the cluster head based on the probability model which will possibly lead to a reduce in network lifetime due to election of cluster head with a least desired location in the network. For wireless sensor networks, the distribution of cluster head selection directly influences the network's lifetime. This paper presents factors which will affect the network lifetime and apply fuzzy logic based cluster head selection conducted in base station. The base station considers two selection criteria from sensor nodes which are energy level and distance to the base station to select the suitable cluster head that will prolong the first node die (FND) time, data stream guaranteed for every round and also increase the throughput received by the base station before FND.

Automatic localization is one of the major issues in Wireless Sensor Networks (WSN). DV-hop algorithm is a well-known localization algorithm in WSN but with limited localization accuracy. In this paper, an improved DV-hop localization algorithm in hybrid optical wireless sensor networks is proposed based on the optimization of the parameters in WSN. Various factors that affect the localization accuracy of the DV-hop algorithm in WSN are investigated, including the communication radius of the node, the number of beacon nodes and the number of the total nodes. As the DV-hop algorithm is applied into hybrid optical sensor and WSNs (O-WSN) with rectangular topology, different parameters have to be optimized accordingly. Simulation results show that the square topology outperforms the rectangle topology more than 45 % under the same network parameters using the improved DV-hop algorithm. Therefore another improved DV-hop called Sub-Square Weighted DV-hop (SSW DV-hop) is proposed for the rectangle topology. Both simulation and experiment results demonstrate that applying the SSW DV-hop algorithmin O-WSNs could significantly improve the localization accuracy.

Cluster based routing approaches have been researched extensively for saving energy of wireless sensor networks (WSNs). In a cluster based routing mechanism, cluster heads (CHs) cooperate mutually to forward their data to the base station (BS) through multi-hop fashion. Due to this process, CHs near to the BS loaded with huge relay traffic and tend to die quickly, which causes partition of the network is popularly known as a hot-spot problem. To tackle the hot-spot problem, in this paper, competitive swarm optimization (CSO) based algorithms have been proposed, jointly call these algorithms as CSO-UCRA (CSO based Unequal Clustering and Routing Algorithms). First, the CH selection algorithm has been presented which is based on CSO based technique, next assign the non-CH sensors to CHs based on the derived CHproficiency function. Finally, a CSO based routing algorithm has been presented. Efficient particle encoding schemes and novel fitness functions have been developed for these algorithms. The CSO-UCRA is simulated extensively with varying number of sensor nodes and CHs for various WSN scenarios, and the obtained results are compared with some recent devised algorithms and standard meta-heuristic based algorithm called PSO-UCRA to show the efficiancy in terms of various performance metrics. CSO-UCRA shows decreased energy consumption of 28.48%, 22.55%, 12.92%, and 3.81%, increased network lifetime of 56.92%, 46.02%, 26.2%, and 8.04% and increased data packets received 73%, 52.5%, 20.8%, and 6.18% over EBUC, EAUCF, EPUC and PSO-UCRA respectively.

Wireless sensor networks are energy constraint networks. Energy efficiency, to prolong the network for a longer time is critical issue for wireless sensor network protocols. Clustering protocols are energy efficient approaches to extend the lifetime of network. Intra-cluster communication is the main driving factor for energy efficiency of clustering protocols. Intra-cluster energy consumption depends upon the position of cluster head in the cluster. Wrongly positioned clusters head make cluster more energy consuming. In this paper, a simple and efficient cluster head selection scheme is proposed, named Smart Cluster Head Selection (SCHS). It can be implemented with any distributed clustering approach. In SCHS, the area is divided into two parts: border area and inner area. Only inner area nodes are eligible for cluster head role. SCHS reduces the intra-cluster communication distance hence improves the energy efficiency of cluster. The simulation results show that SCHS has significant improvement over LEACH in terms of lifetime of network and data units gathered at base station.

A large number of spatially dispersed nodes on the wireless network create Wireless Sensor Networks (WSNs) to collect and analyze the physical data from the environment. The issues that affected the network and had an impact on network energy consumption were cluster head random selection, working node redundancy, and cluster head transmission path construction. Consequently, this energy constraint also has an impact on the network lifetime and energy-efficient routing. Therefore, the primary goals of this research are to decrease energy consumption and lengthen the network’s lifespan. So, using improved optimization algorithms, this paper presents a dynamic cluster head-based energy-efficient routing system. The Improved Coyote Optimization Algorithm (ICOA), in this case, consists of three phases setup, transmission, and measurement phase. The Improved Jaya Optimization Algorithm with Levy Flight (IJO-LF) then determines the route between the BS and the CH. It selects the most effective course based on the distance, node degree, and remaining energy. The proposed approach is compared with traditional methods and the routing protocols Power-Efficient Gathering in Sensor Information Systems (PEGASIS) and Threshold sensitive Energy Efficient Sensor Network protocol (TEEN) during implementation on the MATLAB platform. Performance indicators for the suggested methodology are evaluated based on data packets collected by the BS, energy usage, alive nodes, and dead nodes. The outputs of the suggested methodology performed better than the conventional plans.

ABSTRACTWireless sensor networks, or WSNs, are essential for energy monitoring, home automation, the medical industry, computing, and other fields. The two problems with wireless sensor network are the network lifetime and data transmission delay. The cluster head selection (CHS) is a difficult task. Therefore, a novel Hybrid Archimedes and Arithmetic Optimization Algorithm for Cluster Head Selection and Multipath Routing in Wireless Sensor Network (HYB‐AO‐AOA‐CHS‐WSN) is proposed in this paper. The proposed HYB‐AO‐AOA integrates Archimedes and Arithmetic optimization algorithm (AO‐AOA). The stimulation of sensor nodes initiates the CHS process. The stimulated sensor nodes are evaluated separately for delay, energy, node degree, intercluster distance, and intracluster distance for determining the cluster heads. Then, the multipath routing is handled by the HYB‐AO‐AOA technique. The proposed HYB‐AO‐AOA‐CHS‐WSN model is implemented in MATLAB. The metrics, like delay, number of alive nodes, throughput, and residual energy is examined. The proposed SRD‐DCGAN approach attains 25.72%, 21.32% and 19% higher throughput, 26.56%, 21.35% and 32.09% lower delay than the existing techniques.

Wireless sensor networks are emerging and involved in a wide range of applications. Improving energy efficiency is a significant task in WSNs. Energy consumption is a crucial problem that affects the network lifetime due to energy limitations in sensor nodes. So, it's required to introduce more energy-efficient protocols for WSNs. Different types of energy-efficient solutions exist for WSNs. The energy consumption is optimized and controlled if clustering is an efficient data collection method. The cluster heads aggregate the data from the member nodes and transmit the aggregated data to BS (base station) either in an uncompressed or compressed manner after collecting all data by the cluster-heads (CHs). Due to the heavy inter-cluster relay and aggregation Overhead, earlier death happens to the CHs located very close to the sink. There is a need for an efficient and reliable CH selection algorithms to select reliable CHs to prevent the earlier death of CHs and frequent reselection of CHs. Therefore, in this work, a fuzzy optimal CH selection algorithm is proposed to select the optimal CHs to improve the network's lifetime. The proposed algorithm uses the following multi-objective parameters: cluster load, communication cost, neighbour density, inter and intra-cluster distance, and node energy for CH selection. The modified Emperor Penguin Optimization (EPO) is used in the proposed work to determine an optimal route for transmitting the data from CH to sink. The modified EPO algorithm helps to improve the multi-hop routing efficiency, reduces the cost of long-distance transmission, and decreases energy utilization during data transmission. The modified EPO considers the parameters like link quality factor, relative distance factor, and residual energy factor for relay selection. The proposed technique was compared against earlier methods like Z-DSS, CCR, RETOPSIS, and LEACH-FC. The various parameters like packet delivery ratio, lifetime, energy consumption, end-to-end delay, and Throughput are studied. The evaluation results prove that the proposed mechanism achieves 40% energy efficiency with 60% lesser Overhead and improves the Throughput rate by 50% than the compared methods.

A novel differential evolution based clustering algorithm for wireless sensor networks

This paper proposes a new hierarchical routing protocol dedicated to Wireless Sensor Networks (WSNs). Several solutions have been proposed, but an optimal one, which can be applied in different situations, still remains non-existent. Our proposal takes advantage of existing solutions and proposed a new radial Cluster Head (CH) selection algorithm to ensure the QoS, a good load balancing and to enhance the network lifetime. A performance analysis of our proposal with LEACH, Mod_LEACH and M-GEAR protocols is presented in this paper comparing these protocols using different metrics such as network lifetime, throughput, stability and instability periods, remaining energy, network overhead, etc. The simulations show that our solution outperforms existing solutions due to the radial architecture proposed for CH selection and the use of two transmission levels for intra- and inter-cluster communication. Obtained results show that our proposal improves the network lifetime by 113% compared to LEACH protocol and optimises the energy consumption by more than 62% in comparison to M-GEAR.

In this paper, we propose a novel clustering topology control algorithm named Minimum Spanning Tree (MST)-based Clustering Topology Control (MCTC) for Wireless Sensor Networks (WSNs), which uses a hybrid approach to adjust sensor nodes’ transmission power in two-tiered hierarchical WSNs. MCTC algorithm employs a one-hop Maximum Energy & Minimum Distance (MEMD) clustering algorithm to decide clustering status. Each cluster exchanges information between its own Cluster Members (CMs) locally and then deliveries information to the Cluster Head (CH). Moreover, CHs exchange information between CH and CH and afterwards transmits aggregated information to the base station finally. The intra-cluster topology control scheme uses MST to decide CMs’ transmission radius, similarly, the inter-cluster topology control scheme applies MST to decide CHs’ transmission radius. Since the intra-cluster topology control is a full distributed approach and the inter-cluster topology control is a pure centralized approach performed by the base station, therefore, MCTC algorithm belongs to one kind of hybrid clustering topology control algorithms and can obtain scalability topology and strong connectivity guarantees simultaneously. As a result, the network topology will be reduced by MCTC algorithm so that network energy efficiency will be improved. The simulation results verify that MCTC outperforms traditional topology control schemes such as LMST, DRNG and MEMD at the aspects of average node’s degree, average node’s power radius and network lifetime, respectively.

For the problems of uneven clustering, unreasonable selection of cluster heads and excessive energy consumption in wireless sensor networks for smart grid, an energy efficient clustering algorithm for wireless sensor networks in smart grid (EECSG) is proposed. The algorithm first calculates the optimal number of cluster heads, and then uses k-means algorithm to evenly divide the whole network based on the optimal number of cluster heads. In the selection of cluster head, residual node energy and distance influence factor are introduced to comprehensively select, and finally the data is transmitted stably. The simulation results show that EECSG can dynamically determine the cluster head through the introduction of cluster head selection factor, which reduces the energy loss during data communication in the network and improves the overall life of the wireless sensor network.

A large number of small sensors in wireless sensor network are battery-powered; one of the most important design criteria for this type of network is energy efficiency. Clustering provides an effective way for extending the lifetime of a sensor network. In this paper, we propose a maximum-votes and load-balance clustering algorithm (VLCA) for wireless sensor network Each sensor collects votes from their neighbors and calculates the total vote received. The more votes a sensor accumulates, the more important it is in the whole network During the clustering phase, sensors compete with each other based on the total votes each has received. The algorithm is completely distributed, locating-unaware and independent of network size and topology. Simulation results show that our VLCA can reduce the number of clusters by 20-50% and prolong the lifetime of a sensor network.