Event Distortion-Based Clustering Algorithm for Energy Harvesting Wireless Sensor Networks

The technology of wireless sensor networks (WSNs) is in constant development and it made great progress in many applications. One of the most popular problems in WSNs is the limited energy storage power at every sensor node. This paper aims to propose and develop a new distributed clustering algorithm for energy harvesting wireless sensor networks denoted by DEH-WSN (Energy Harvesting for Distributed Clustering Wireless Sensor Networks Protocol) that relies on matching between clustering and energy harvesting in a distributed topology. DEH-WSN uses initial and residual energy capacity of the nodes to choose cluster heads. Simulation results prove that the proposed method increases network lifetime and the effective throughput.

Designing routing protocols for Energy Harvesting- Wireless Sensor Network (EH-WSN) has two key challenges like improving harvested energy utilization and examination harvested energy utilization. However, the clustering and routing phases of the clustering-based routing protocols employed in EH-WSN need a lot of message overhead. Cluster Head Selection (CHS) is a crucial component of clustering that improves energy efficiency. An Enhanced Energy Harvesting Clustering (EEHC) protocol with two phases for cluster setup and data transmission is presented in this research. Sensor nodes are grouped into clusters during cluster construction, and CHS using the Gaussian Harris Hawks Optimizer (GHHO) algorithm is used for data transmission. Hawks chasing behaviours serve as the search agent in the GHHO algorithm, while prey serves as the ideal CH position. Data from each cluster node is collected by CHS, which then sends the aggregated data to the base station. Following CHS, connecting Cluster Member (CM) to a suitable CH is done using a variety of factors like energy, distance to neighbours, distance to the Base Station (BS), node density, and Signal-to-Noise Ratio (SNR). Each CM awakens during its designated working time slot during the DT stage, and transfers the data it has gathered towards the CH of the same cluster. A mobile sink is employed to enhance network performance and relay transmission distance is changed to match the amount of gathered energy. Residual Energy (RE), Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), Network Lifetime, and average delay are metrics used to measure the results of clustering methods.

Renewable energy harvesting schemes in wireless sensor networks: A Survey

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.

In this paper, we propose an optimal data forwarding strategy to minimize sleep duration time for one-dimensional wireless sensor networks with energy harvesting sensor nodes. Based on the values of remaining energy and harvested energy of sensor nodes, the base station (data sink) determines the sleep duration time and the data forwarding strategy for the next data gathering round. According to the proposed algorithm, each sensor node can act as a relay node that receives and forwards the data toward data sink. This strategy also guarantees that every sensor nodes do not reach the energy depletion while minimizing sleep duration time, resulting in the maximum data gathering frequency. From the mathematical analysis and the numerical simulation, the proposed strategy shows 2.5% to 50% smaller sleep duration time compared to the simple data forwarding algorithms such as single hop or multi-hop data forwarding only.

One of the main emphases of the prevalent clustering and routing algorithms in wireless sensor networks is efficient energy utilization aimed towards enhanced network lifetime. In this paper we are proposing a clustering approach where the sensor nodes are clustered together based on a defined spatial correlation and the geographical location of each sensor nodes. The Cluster Head or Cluster Leader along with routing path is decided by our proposed algorithm based on the estimated chance-value during each rounds of data transfer. Thus in our scheme a multi-hop routing is effected wherein either two-level or three-level hops are dynamically chosen based on the spatial correlation existing between the nodes for the data transfer from the sensing node to the sink. It has been observed that our proposed algorithm outperforms the standard algorithms namely Low Energy Adaptive Clustering Hierarchy (LEACH), Stable Election Protocol (SEP) and Distributed Energy Efficient Clustering (DEEC) Protocol with regards to system throughput and Network Lifespan of WSN.

Wireless Sensor Network (WSN) includes of many nodes by restricted energy resources. Energy efficiency and harvested energy are major important issues in the WSN. Studies recently conducted have demonstrated that clustering is an effective way to increase energy efficiency. Energy Harvesting- Wireless Sensor Network (EH-WSN) is a flexible strategy for even clustering and Cluster Head (CH) selection is helpful to maximize network constancy and energy efficiency. In this paper, Energy Harvesting Possibilistic Fuzzy C-Means (EHFPCM) clustering is introduced to improve harvested energy usage by maintaining the consistency, connectivity, and balancing of harvested energy consumption in EH-WSN. It is based on Data Transmission (DT) and Cluster Establishment (CE). During CE, PFCM clustering is introduced for cluster formation. PFCM clustering divides the network into clusters. Each area forms a group and chooses one or more CH based on the multi-criteria like energy, distance to neighbors, distance to the Base Station (BS), and network load. In a cluster, the Fuzzy Election Based Optimization Algorithm (FEBOA) selects the CH according to the multi-criteria. It desires to receive packets from Cluster Member (CM), aggregate the received packets, and subsequently forward it to DT. DT, every CM wakes up during its designated working time and transmits the data it has gathered to the CH in the cluster. Lastly, measures such as Residual Energy (RE), Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), energy consumption, and average delay for transmission are used to measure the results of routing protocols.

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.

Radio transmission and reception consumes a lot of energy in a wireless sensor network (WSN), which are made of low-cost, low-power, small in size, and multifunctional sensor nodes. Thus, one of the important issues in wireless sensor network is the inherent limited battery power within the sensor nodes. Therefore, battery power is crucial parameter in the algorithm design in maximizing the lifespan of sensor nodes. It is also preferable to distribute the energy dissipated throughout the wireless sensor network in order to maximize overall network performance. Much research has been done in recent years in the area of low power routing protocol, but, there are still many design options open for improvement, and for further research targeted to the specific applications, need to be done. In this paper, we propose a new approach of an energy-efficient homogeneous clustering algorithm for wireless sensor networks in which the lifespan of the network is increased by ensuring a homogeneous distribution of nodes in the clusters. In this clustering algorithm, energy efficiency is distributed and network performance is improved by selecting cluster heads on the basis of (i) the residual energy of existing cluster heads, (ii) holdback value, and (iii) nearest hop distance of the node. In the proposed clustering algorithm, the cluster members are uniformly distributed and the life of the network is further extended.

The wireless sensor networks (WSNs) are usually deployed among widely area for environmental monitoring, data collection and other applications. In order to reduce the energy consumption and extend the lifetime of WSNs, as well as receive the data quickly, a source-based data aggregation algorithm (SBDA) for clustering wireless sensor networks is proposed. The method aggregated the data from sensor nodes and marked different transmission priority for the data. Simulation results show that, compared to existing protocols, the proposed method achieves a longer life time, and a lower packet drop ratio.

In order to gather information more efficiently, wireless sensor networks (WSNs) are partitioned into clusters. The most of the proposed clustering algorithms do not consider the location of the base station. This situation causes hot spots problem in multi-hop WSNs. Unequal clustering mechanisms, which are designed by considering the base station location, solve this problem. In this paper, we introduce a fuzzy unequal clustering algorithm (EAUCF) which aims to prolong the lifetime of WSNs. EAUCF adjusts the cluster-head radius considering the residual energy and the distance to the base station parameters of the sensor nodes. This helps decreasing the intra-cluster work of the sensor nodes which are closer to the base station or have lower battery level. We utilize fuzzy logic for handling the uncertainties in cluster-head radius estimation. We compare our algorithm with some popular algorithms in literature, namely LEACH, CHEF and EEUC, according to First Node Dies (FND), Half of the Nodes Alive (HNA) and energy-efficiency metrics. Our simulation results show that EAUCF performs better than the other algorithms in most of the cases. Therefore, EAUCF is a stable and energy-efficient clustering algorithm to be utilized in any real time WSN application.

Wireless sensor networks area collection of a large number of unattended sensor nodes. The sensor nodes are dynamically deployed in the region. In wireless sensor networks, the unlicensed sensor nodes use their temporarily available vacant licensed channels or spectrum band in a telecommunication network. The many open challenges with issues such as adaptive in self-organisation, fault-tolerance and network scalability with cooperative spectrum sensing and sharing are identifying new challenges in wireless sensor networks. In this paper, we proposed the bio-inspired adaptive routing algorithm for cognitive radio wireless sensor networks. The utilisation of the foraging approach of ANTs and BEEs for making the decision of using the available spectrum sensing and sharing of channels over multi-hops communication, provides the method to utilise idle channel and decrease the delay for communication in wireless sensor network. In this paper, we design the algorithm to solve the issue of wireless sensor network and performance of classical routing protocol LEACH and cognitive radio sensor networks (CRSN) evaluates the parameters which proof the self-organisation routing algorithm.

Energy harvesting aware topology control with power adaptation in wireless sensor networks

An energy-saving routing architecture with a uniform clustering algorithm for wireless body sensor networks

Sensors are capable of performing different tasks such as receiving various environmental information and have generated the idea of creating and expanding the traditional networks to Wireless Sensor Networks (WSN). A WSN include various sensor nodes which are distributed in the environment to gather data. The sensor node’s location is not necessarily defined and not specified. Such a feature allows us to release them in hazardous or inaccessible places. On the other hand, this means that the WSN protocols and algorithms must have the ability to self-deny. Other unique features of WSNs are the ability of sensor nodes to collaborate and coordinate with each other. A processor is assigned to each sensor node which initially performs a series of acquired simple information processing to transmits the semi-processed data instead of sending all raw data to the center, Base Station (BS) or the sink node. Fault detections in WSN are of particular importance due to unique technical and conceptual challenges. In this paper, due to the specific limitations and operating conditions of wireless sensor networks, different fault detection algorithms were examined. The best way to improve the ability of fault detection is to reduce energy consumption in sensor nodes. In such techniques, using the residue number system in the wireless sensor network structure improves repair and fault detection. Moreover, modules are attached whenever we require distinguishing and correcting the information security in the number system. Although there are various algorithms yet the Residue Number System algorithm has higher performance.