Comparative Analysis of Bio inspired Optimization Techniques in Wireless Sensor Networks with GA-PSO Approach

Among important issues in the current energy-efficient routing protocols for wireless sensor networks (WSNs) based on clustering approaches are how to determine the optimal number of clusters, how to generate clusters and how select cluster-heads to improve WSNs performance. These approaches aim to reduce energy consumption of the WSN nodes and to extend the network lifetime. This paper reviews and compares the performance of three clustering techniques for determining number of clusters: rule of thumb, elbow, and silhouette. Each of these techniques involves a distributed K-means approach to generate clusters. Moreover, we compare these three clustering methods with low energy adaptive clustering hierarchy (LEACH), Imp LEACH and MODLEACH in terms of network lifetime, energy consumption and the number of packets sent to the base station (BS). The results obtained indicate that rule of thumb method provides better performance compared to other clustering techniques in terms of energy consumption and network lifetime.

Wireless Sensor Networks (WSNs) are severely resource-constrained in terms of processing power, radio range and battery. Due to this reason, the clustering-based routing protocols designed for the WSN have traditionally focused on improving the network lifetime. However, due to the role of WSN in the future Internet of Things (IoT) based applications which would comprise of devices with Multiple Input Multiple Output (MIMO) antennas, maximizing network coverage would be equally important as network lifetime. Balanced Energy Efficient Multi-hop (BEEM) is a clustering-based routing algorithm which was designed to meet the above mentioned constraints. The BEEM protocol enhances the network lifetime while maintaining network coverage through balanced sensor distribution. However, BEEM protocol considers static WSN only and does not take into consideration mobility. As mobility is an important feature in a number of IoT applications therefore there is a need to evaluate BEEM algorithm for Mobile WSN in order to assess its applicability to such applications. In this paper, we evaluate BEEM for mobile-sink-based WSNs which takes into consideration the sink mobility as random positioning of sink inside the grid; we call it BEEM-M. We carry out simulations using MATLAB and compare BEEM-M against BEEM and HEED algorithms. Our results show that BEEM-M outperforms both algorithms in terms of network lifetime as well as network coverage. This shows that BEEM is suitable for sink mobility-based applications.

In the wireless sensor networks, sensor deployment and coverage are the vital parameter that impacts the network lifetime. Network lifetime can be increased by optimal placement of sensor nodes and optimizing the coverage with the scheduling approach. For sensor deployment, heuristic algorithm is proposed which automatically adjusts the sensing range with overlapping sensing area without affecting the high degree of coverage. In order to demonstrate the network lifetime, we propose a new heuristic algorithm for scheduling which increases the network lifetime in the wireless sensor network. Further, the proposed heuristic algorithm is compared with the existing algorithms such as ant colony optimization, artificial bee colony algorithm and particle swarm optimization. The result reveals that the proposed heuristic algorithm with adjustable sensing range for sensor deployment and scheduling algorithm significantly increases the network lifetime.

Wireless sensor networks are the sensors which are acclimated to sense the environmental condition like temperature, pressure, sultriness, moisture etc, sensing the environment parameters and sending them to the gateway and retrieving the aggregated data from the gateway to the end user. Power is the major constraint in wireless sensor networks. One must need to reduce the power consumption. Wireless sensor networks have sensor nodes in which each node has a processor, antenna and a battery. The batteries consume power so that we require increasing the lifetime of the battery for that some optimization techniques are required to reduce the power consumption. There are some techniques which are inspired from the lifestyle of animals. In this paper proposing an optimization technique which is inspired by the animal demeanor which reduces the power consumption of the sensor nodes which is particle swarm optimization (PSO) technique. PSO is inspired by the convivial demeanor of birds or schooling of fish. By utilizing this bio-inspired technique we can reduce the power consumed by the sensor nodes and at the same time lifetime of the batteries present in the sensor nodes are increased.

Wireless sensor networks (WSNs), often composed of a large number of sensor nodes with limited power, have been widely used for environmental monitoring and battlefield surveillance. A basic operation in such networks is data gathering. In the applications of data gathering without aggregation, the 1-hop nodes always incur much heavier traffic load compared with other nodes, which determine the lifetime of the whole network. Due to the energy constrained nature of sensor devices, energy-efficient methods should be employed for data gathering. In this paper, we propose a Load-Balanced and energy-efficient Tree (LBT) algorithm to maximize the lifetime of WSNs, which takes into account the load balance and energy efficiency of 1-hop nodes. To the best of our knowledge, we are the first to present the upper bound of network lifetime for data gathering without aggregation with tree-based topology. Simulation results demonstrate that our algorithm utilizes up to 98 % of the total energy of the 1-hop nodes and outperforms the state-of-the-art algorithms in terms of network lifetime. Furthermore, LBT can achieve a lifetime consistently close to the upper bound.

In a Wireless sensor network, the energy hole problem is a severe issue that forces the nodes near the sink to exhaust their energy sooner than the nodes deployed in the outer region, resulting the reduced the network lifetime. To overcome this problem, a novel clustering scheme with load balancing is proposed for a corona model network, where the coronas are divided into sector and form homogenous clusters. In the homogenous cluster, some nodes inside the circular centroid region (CR) forms cluster head (CH) to control network operation. It is important that the Load handled by the cluster heads are to be balanced by means of mixed routing strategy together with data aggregation. It is seen that this technique balances the energy in the network. The simulation result shows that this technique out performs other clustering scheme in terms of network lifetime and energy dissipation.

In most wireless sensor network (WSN) applications, data are typically gathered by the sensor nodes and reported to a data collection point, called the sink. In order to support such data collection, a tree structure rooted at the sink is usually defined. Based on different aspects, including the actual WSN topology and the available energy budget, the energy consumption of nodes belonging to different paths in the data collection tree may vary significantly. This affects the overall network lifetime, defined in terms of when the first node in the network runs out of energy. In this paper, we address the problem of lifetime maximization of WSNs in the context of data collection trees. In particular, we propose a novel and efficient algorithm, called Randomized Switching for Maximizing Lifetime (RaSMaLai) that aims at maximizing the lifetime of WSNs through load balancing with a low time complexity. We further design a distributed version of our algorithm, called D-RaSMaLai. Simulation results show that both the proposed algorithms outperform several existing approaches in terms of network lifetime. Moreover, RaSMaLai offers lower time complexity while the distributed version, D-RaSMaLai, is very efficient in terms of energy expenditure.

Wireless Sensor Networks (WSNs) consist of a large number of low powered nodes that need to operate for months unattended. Since modern WSNs are used in various applications, their topology is becoming complicated. Due to limited precision of deployment, holes may occur in the network, which often lead traditional Greedy Forwarding algorithms to fail. Thus, bypassing the holes is one of the important issues for WSNs. Since each node has limited energy, its energy consumption needs to be optimized to prolong network lifetime. In the well-known Virtual Position (ViP) scheme, each node routes data using virtual positions instead of actual geographic positions to improve the packet delivery rate. A Hole-bypassing Routing with Context-awareness scheme achieves balanced energy consumption by changing current path to one of the candidate paths, based on the residual energy of nodes. However, this scheme tends to extend the size of holes. Since existing hole detour schemes that do not consider efficient energy consumption, they cause imbalanced energy consumption and make network lifetime relatively shorter than other hole detour schemes. Similar to ViP, our scheme uses virtual positions to bypass holes. However, the virtual positions are computed using both geographic positions and the residual energies of neighbor nodes. Our approach outperforms the ViP scheme in terms of network lifetime and hole extension.

Wireless sensor network consists of large number of sensor nodes which is used to capture environmental conditions, process and transfer it to base station.These sensor nodes are sensitive to energy consumption and gets exhausted on working.So we need to emphasize on reducing energy dissipation of sensor nodes in order to improve network lifetime.Various routing protocols are developed to enhance network scalability and stability.The cluster based routing is an efficient way of reducing energy dissipation by limiting data transmission from nodes to base station.For this purpose we proposed a new routing method which improves the life span of network, its data transmitting capability with low energy consumption.It is an improvised version of LEACH protocol which conserves energy by load balancing .Multi hop routing is proposed for the transmission of data.Reduction of energy consumption is achieved with the help of TDMA scheduling.Time division multiple access (TDMA)-based MAC can potentially reduce the delay and provide real-time guarantees as well as save power by eliminating collisions.To keep network alive we had added a random node on replacement of dyeing node.The performance evaluation of protocol is based on simulation test performed in network simulator 2 (NS2) to prove the effectiveness of this proposed method in terms of network lifetime and energy efficiency.

One of the important parameter to evaluate the performance of wireless sensor network (WSN) is overall network Load balancing is the energy maintenance method which can distribute huge data traffic evenly to prolong the whole network lifetime. In this paper, we present the load balancing system with mobile robot as a subnetwork's mobile sink to maximize network lifetime. The field test and simulation results demonstrate the performance of our system.

This paper presents a radial cluster heads selection mechanism for homogenous wireless sensor networks. It aims to ensure a good load balancing and enhance the network lifetime. A performance comparison between LEACH, Mod_LEACH, Multi-Chain-PEGASIS, M-GEAR protocols and our proposal in term of network lifetime, stability/instability periods and the evolution of the number of cluster heads is carried out in this work. Obtained results show that developing an efficient cluster heads selection algorithm enhances considerably the network lifetime and the QoS in the network. Simulations results under NS3 simulator showed that our proposal can extend the network life more than 300% compared to the famous LEACH protocol and ensure a network lifetime of about 5000 rounds while it does not exceed 2500 rounds using M-GEAR.

Wireless sensor networks (WSNs) are often deployed in harsh environments with little human control, thereby necessitating self organizing and maintenance mechanisms for medium sharing and data routing. Moreover, since sensor nodes operate on energy-limited batteries, designing energy-efficient routing techniques that extend the network lifetime is of a paramount importance. In this paper, we propose WCDS-DCR, a fully distributed, data-centric, routing technique that makes use of an underlying clustering structure induced by the construction of WCDS (Weakly Connected Dominating Set) to prolong network lifetime. WCDS-DCR aims at extending network lifetime through the use of data aggregation (based on the elimination of redundant data packets) by some particular nodes. It also utilizes both the energy availability information and the distances (in number of hops) from sensors to the sink in order to make hop-by-hop, energy-aware, routing decisions. Simulation results show that our solution is scalable, and outperforms existing schemes in terms of network lifetime.

SummaryIn energy‐constrained military wireless sensor networks, minimizing the bit error rate (BER) with little compromise on network lifetime is one of the most challenging issues. This paper presents a new relay selection based on fuzzy logic (RSFL) scheme which provides balance between these parameters. The proposed scheme considers node's residual energy and path loss of the relay‐destination link as the input parameters for the selection of the relay node. The relay node selection by fuzzy logic is based on prioritizing higher residual energy and minimum path loss. To evaluate the performance on wireless sensor network, we compare the proposed scheme with the three existing relay selection strategies, ie, random, maximum residual energy based relay selection (MaxRes), and minimum energy consumption based relay selection (MinEnCon). The simulation results of the proposed scheme in terms of network lifetime, BER, Network Survivability Index (NSI), and average energy of network nodes have been presented and compared with different relay selection schemes. The simulation results show that the proposed RSFL scheme has the lowest BER, moderate network lifetime, average energy, and NSI.

A Wireless Sensor Network (WSN) is a group of sensors which communicate with each other and perform some specific task. Clustering is used to conserve energy in a WSN. In this work, the aim is to minimize the energy consumption and maximize the network lifetime of a homogeneous WSN using PSO (Particle Swarm Optimization) based Clustering algorithm in conjunction with quantum computing. In quantum computing, a bit is known as a qubit and it can exist in the following states: a ‘0’, a ‘1’ or a superposition of ‘0’ and ‘1’. In this chapter, the Quantum Computing based PSO clustering algorithm for Optimizing Energy consumption and Network lifetime (QCPOEN) algorithm for homogeneous wireless sensor networks is proposed. The proposed algorithm is compared with the PSO-ECHS algorithm and the LEACH algorithm. The superiority of the algorithm can be verified from the results.

Wireless sensor networks (WSNs) are densely deployed for monitoring of environments and physical conditions in an extensive range of applications. Network lifetime and energy consumption are the primary performance evaluation metrics for WSNs. In this paper, an adaptive soft thresholding-based energy efficient sensor network (ASTEEN) protocol is proposed to increase the network lifetime by reducing the number of transmissions of non-critical data. It consequently reduces the consumption of communication energy. The performance of the proposed protocol is investigated with correlated and uncorrelated sensed data, and it is compared with the other related and conventional protocols. Simulation results show that the performance of the proposed ASTEEN routing protocol in terms of network lifetime and energy consumption significantly outperforms those of the other protocols, especially with correlated sensed values.