The Use Of Wsns

Published Date: 02 Nov 2017

Dynamic Source Routing (DSR) algorithm which computes a new route when the loss of packet occurs has been presented by N. Chilamkurti et.al [88]. To find that the packet loss is caused by whether node failure or it is due to the result of congestion which causes DSR to find a new route there is not any in-built mechanism in DSR algorithm. Thus when DSR used in the wireless sensor networks it leads to inefficient energy utilization. Authors developed cross layer optimization mechanism that widens DSR to enhance its routing energy more proficiently by lowering the frequency of recalculated route in the proposed work. This approach makes DSR to start a route discovery only when the link breakdown takes place. Extensive simulation has been done to calculate the performance of the cross layer DSR routing algorithm by the authors. The results which is obtained by simulation of our extended DSR routing algorithm shows that the frequency is 50 % decreased with the new routes which are recomputed when compared with the original DSR protocol. The researchers exposed that with the proposed cross layer DRS that distinguish between congestion and the link failure and the new routes are recomputed only for the link failure.

A multimedia sensor node that can extensively improve the capability of wireless sensor network has been used by Lei Shu et.al [89] for event description. The WSN’s cannot be use effectively for extremely long times in a number of scenario we can take an erupting volcano as an example for it. Instead of this the WSNs are aimed to transfer constant and reliable data as possible in the large number within an expected lifetime. A proficient collection of multimedia data within an expected lifetime in WSNs has been expected by the authors in this paper. By taking into consideration the communication between physical, network and the transport layer, an adaptive method to adjust the transfer radius and the data generating rate adjustment (RRA) dynamically is proposed which is based on the cross layer design. By using the minimum data generation rate the designer first minimize the end to end data transfer delay in WSNs. A finest transmission radius thus can be derived or obtained in this phase. Then by using this transmission radius, designers adaptively fix the rate of generating data to increase the amount of collected data. By dynamically fixing the transmission radius of the sensor nodes and increasing the data generation rate of the source node, the proposed RRA scheme can enhance the data collection performance effectively in wireless multimedia sensor networks (WMSNs) as shown by the simulation results.

Jianping Yu et.al [90] presented the multi-constrained any cast routing is an important issue in the ad hoc networks because of the mobility of nodes. To improve the any-cast success rate and shorten the any-cast delay when considering the special constraints of the bandwidth and delay during the network deployment period, an any-cast algorithm inspired by swarm intelligence of ants is proposed, in which the improved pheromone diffusion mechanism is adopted to promote the performance of any-casting emerging in self-organized way from the positive interaction of ants. The simulation results demonstrated that not only the performance of any-cast success rate can be improved but also significant smaller delay can be achieved by the proposed algorithm when compared with other any-cast routing algorithms.

Xin Fei et.al [91] proposed in the densely deployed wireless sensor networks, sensors are scheduled over time in order to maintain the coverage while saving energy of networks. In this article, Authors investigate the coverage-aware scheduling problem using genetic algorithms. Sensors are optimally scheduled in different time slots to maximize the overall coverage under the given k-cover requirement and lifetime of networks. A set of simulation is carried out. The simulation result shows that, using the optimal schedule generated by genetic algorithm, this algorithm can optimize the coverage performance of wireless sensor network in terms of overall coverage rate and number of active sensors.

The static shortest path (SP) problem is well addressed in the recent years with the use of intelligent optimization system for e.g. genetic algorithm (GAs), artificial neural networks and particle swarm optimization etc. has been presented by Shengxiang Yang et.al [92]. With the development in the field of wireless communication, many more mobile wireless network has appeared now a day such as wireless sensor networks (WSNs), mobile networks [mobile ad hoc networks (MANETs)], etc. The most important feature in the wireless mobile network id the dynamics of the topology since the network topology changes over a specific period of time due to node mobility or energy conservation. Thus the SP routing problem in the MANETs seems to be a dynamic optimization problem. The use of Gas with memory scheme and immigrants to sort out the dynamic SP routing problem in MANETs has been proposed by the authors. MANETs are considered to be the target system for the authors since it is the new generation wireless network. These memory based and immigrants GAs adapts the environmental change i.e. change in network topology quickly and produces a high quality solution after every change and is shown in the experimental results.

One of the most difficult tasks in the area of WSNs is to control the power consumption of the batteries and the higher network lifetime and is presented by Akira Mutazono et.al [93]. For the sensor networks which contain large number of sensor nodes, centralized control is not suitable hence we use self organized method. Research work on bio inspired self organization methods seek attention because it has the potential to be applicable in the WSNs. A focus on calling behavior of Japanese tree frogs has been considered by the authors. These frogs presents a type of behavior which is known as "satellite behavior", in this type of behavior a frogs stops calling if it detects the call of neighboring frog. This behavior can be used in designing an energy efficient sleep control method which gives adaptive operation periods. A self organizing scheduling method has been presented by authors who are inspired by the frogs calling behavior for data transfer in WSNs which is energy efficient. The proposed sleep control mechanism increases the network lifetime by a major factor of 6.7 as compared with scheme without sleep control for a coverage ratio of 80% is shown in the simulation result.

Wireless sensor networks (WSNs) are the networks of autonomous nodes which are used for monitoring an environment has been proposed by Raghavendra V. Kulkarni, and Ganesh Kumar Venayagamoorthy [94]. The designers of WSNs confront many challenges that generally come from the communication link failure, limited energy and memory and computational constraints. Many of the problems in WSNs are treated as multidimensional optimization problems and the same are approached through bio-inspired schemes. A simple, efficient and effective optimization algorithm has come under consideration and known to be Particle swarm optimization (PSO). It has been developed to solve the problems such as node localization, optimal deployment, data aggregation and clustering in the WSNs. In this the authors mainly focused on the problems in WSNs and introduced PSO to discuss its compatibility with WSN application.

A SEA (Sensor Equipped Aquatic ) swarm which is a collection of mobile sensors and works underwater, moves in a group with the water current and enables a 4D (space and time) monitoring of the local underwater events e.g. contaminants and intruders is proposed by Luiz Filipe M. et.al [95]. For the quick alert coverage, mobile sensors routes the events to mobile sinks through geographic routing and this is known to be the most accurate under mobility and scarce acoustic bandwidth. For a packet to be routed to the destination by geographical routing, it is must to know the exact location of the destination, and this is done by a location service which returns the location of a requested node. Here, the main goal of the author is to design such location service for the SEA swarm, and also they examine different design choices to examine a proficient location service in SEA swarm conditions. Ad hoc network location service protocols which are conventionally used cannot be used directly as, the whole swarm moves along the water current. Maintaining location information in a 2D plane is a much better design choice is proved by the authors and following this they propose a bio inspired location service named as Phero-Trail location service protocol. In this, the information of location is stored in a 2D shape of a SEA swarm and a mobile sink is there which uses its trajectory which is projected to a 2D shape to maintain location information, this makes the mobile sensors to effectively locate a mobile sink. The outcome has showed that the Phero-Trail has a better performance than the existing approaches.

To solve the combinational optimization problem such as routing in computer networks, an algorithm names as Ant Colony Optimization (ACO) is presented by Maumita Bandyopadhyay, and Parama Bhaumik [96] which is stochastic process. The food accumulation method of the ant community is the main idea behind this optimization method. Based on the idea of individual node’s location for routing of packets in mobile ad-hoc networks a one based routing algorithm is built. In this the nodes location can be further used to discover the routes by the Ants in optimized way. A Position based routing algorithms (POSANT) has few major loopholes in finding the route from source to destination as it never guarantees the shortest path, in some cases it is able to find it. On the other hand, routing algorithms which are also based on the ACO find routing path which is the shortest. The main drawback of these algorithms that we have to sent number of control messages causing the long delay before the routes get established from source to destination. Authors used Zone based ACO using clustering which assures to find the shortest route with the help of DIR principle ( in this, the source node sends messages to various neighbors and the node whose direction is nearest to the direction of the destination automatically get selected as the next hop forwarding note) with minimum slide for route discovery and mobility management. It is not required in clustering to consider zone related information of each node in finding the shortest path unlike other zone based approach. To minimize the overhead, this proposed algorithm which contains new algorithm for mobile ad hoc network by the combination of Ant Colony approach and Zone based routing approach which uses cluster to get shortest path which sends the small number of control messages. It is shown in the simulation that the Zone based ant colony routing algorithm has short route establishment overhead when compared to other zone based ant colony algorithms in mobile scenario.

The use of WSNs in sensitive application which includes defense, healthcare, early bushfire detection, habitat monitoring etc, needs a careful consideration which is presented by Tanveer A Zia and Md Zahidul Islam [97]. WSN’s are very much open to security attacks due to their nature of being wireless and deployment. This security attack is due to the fact that sensor nodes left unattended after deployment of the network. Thus the insecurity in the wireless communication made WSNs worse and vulnerable. Various measures has been proposed by authors to overcome the threats in sensor networks. However, it very costly to overcome this security concerns, here notion of trust has been studied by authors and according to that they proposed a solution which is based upon communal reputation and individual trust (CRIT) in sensor nodes. Simulation result and the performance analysis determine the capability of this study.

Changlei Liu and Guohong Cao [98] Self-monitoring the sensor statuses such as liveness, node density and residue energy is critical for maintaining the normal operation of the sensor network. When building the monitoring architecture, most existing work focuses on minimizing the number of monitoring nodes. However, with less monitoring points, the false alarm rate may increase as a consequence. In this paper, authors study and show the fundamental tradeoff between the number of monitoring nodes and the false alarm rate in the wireless sensor networks. Specifically, authors propose fully distributed monitoring algorithms, to build up a poller-pollee based architecture with the objective to minimize the number of overall pollers while bounding the false alarm rate. Based on the established monitoring architecture, they further explore the hop-by-hop aggregation opportunity along the multihop path from the polee to the poller, with the objective to minimize the monitoring overhead. Authors show that the optimal aggregation path problem is NP-hard and propose an opportunistic greedy algorithm, which achieves an approximation ratio of 5/4. As far as we know, this is the first proved constant approximation ratio applied to the aggregation path selection schemes over the wireless sensor networks.

A group of birds which self organizes into a V formation when they have to travel to long distance is presented by Federico S. et.al [99]. By this formation they save energy by using the advantage caused by up wash which is generated by the neighboring birds. Authors’ uses a scheme for the up wash caused by a flying bird and shows that a group of birds can self organizes into a V formation if each and every bird has to process spatial and network knowledge through an adaptive diffusion process. Birds are required to achieve measurement of the up wash and also have to use the information from the neighboring birds in the proposed diffusion algorithm. The results obtained have several interesting applications such as, first a simple diffusion algorithm that can reckon for self organization in the birds, this algorithm runs in real time and fully distributed, second, is that the birds can self organized based on the up wash which is generated by the neighboring birds, third is to achieve flight formation information sharing is necessary by the birds. A modification in the algorithm is also proposed which shows that the bird can also get organized into a U formation after starting from V formation. According to the authors this new formation is more effective as it leads to equalization effect i.e. every bird in the group observes same wash up.

Mehmet E. Aydin et.al [100] done much work is underway within the broad next generation technologies community on issues associated with the development of services to foster collaboration via the integration of distributed and heterogeneous data systems and technologies. In previous works, authors have discussed how these could help coin and prompt future direction of their usage (integration) in various real-world scenarios such as in disaster management. This paper builds upon on previous works and addresses the use of learning agents called learning birds in modeling the process of data collection using wireless sensor networks, Specifically, learning birds are some sort of nature-inspired learning agents collaborating to create collective behaviors. As an artificial bird flock, the swarm members collaborate in positioning while moving within a particular environment. In order to improve the diversity of the flock, each individual needs learning the how to position relatively to its neighbors. Q learning is a very famous reinforcement learning algorithm, which offers a very efficient and straightforward learning approach based-on gained experiences. Therefore, a swarm of birds collaborating and learning while exchanging information to position offers a very useful modeling approach to develop ad- hoc based mobile data collection tools. To achieve this, authors use a disaster management scenario.

Saeed Mehrjoo et.al [101] extending the lifetime of wireless sensor networks remains in the center of attention when talking about wireless sensor network issues. As lifetime is directly dependent upon the energy supplies of the nodes, optimization of node energy consumption is a robust approach to contribute to the overall network lifetime. Network clustering is one of the potential approaches to perform the optimization. However, optimum clustering of wireless sensor networks is an NP-Hard problem. To overcome this problem, a hybrid algorithm based on Genetic Algorithm and Artificial Bee Colony is proposed in this paper. The algorithm resolves the issue through finding the optimal number of clusters, cluster heads and cluster members. Simulation results reveal that this algorithm outperforms LEACH and Genetic Algorithm based clustering scheme.

Jiang Du and Liang Wang [102] ordered to make good use of the limited energy, ant colony optimization (ACO) was applied to inter-cluster routing mechanism. An uneven clustering routing algorithm for Wireless Sensor Networks (WSNs) based on ant colony optimization (ACO) algorithm utilized the dynamic adaptability and optimization capabilities of the ant colony to get the optimum route between the cluster head. Meanwhile, it organized different cluster in different size based on the distance between cluster heads and sink node, and clusters closer to sink had smaller sizes than those farther away from the sink, thus the closer cluster heads could preserve energy for the inter-cluster data forwarding. Simulation result indicates that the algorithm effectively balances the network energy consumption and prolongs the network life cycle compared with LEACH and PARA.

LI Jinpeng and GAO Li [103] ordered to hold the hardware cost is not the premise of improving the positioning accuracy of sensor nodes, the authors proposed the node localization research of the underground wireless sensor networks based on DV-Hop and Ant Colony Optimization, this method of non-ranging DV-Hop algorithm calculate the distance between unknown nodes and beacon nodes, with the method of triangular positioning algorithm to determine the location of unknown nodes, and through the ant colony optimization to improve accuracy of the unknown node location. To verify the feasibility of the method, with the comparison of the traditional orientation algorithms and the improved algorithm by MATLAB simulation, experimental results show that the improved algorithm has higher accuracy.

WSNs get a large acceptance in the range of user application due to its ability to control ambient conditions without the need of human contribution and it’s presented by Santosh Kulkarniand Prathima Agrawal [104]. WSNs practically use large number of highly deployed, low power, low cost and multi functional sensor nodes which interacts each other through wireless link. Ther are various limitations in the individual resources of the sensor nodes but if they cooperate among each other then it can lead to a powerful and large ad hoc computing system. Working towards this aspect of the sensor nodes authors come across that when compared to the data generated by a node, the storage capacity of a single node is very limited. When this capability of limited storage coupled with the temporary sink nodes and the changing importance of data collected means that, the data generated needs to be stored locally within the network so that we can retrieve it later. The authors introduce a cooperative storage scheme which is based upon the behavior of Honey and Ants in the real world to achieve the above target. This proposed method is based upon the use of data migration technique and relies heavily on migratory transportation unit known as Ant Agents to search the vacant storage resources within the network. It is shown by the simulation result that the cooperative data gathering is beneficial for storage controlled nodes in WSNs.

For sensor management of the multi sensor networks a Swarm Intelligence based approach has been presented by K. Veeramachaneni and L. A. Osadciw [105]. A varying sensor configuration and fusion method is calculated by Swarm agents and finest configuration and fusion method evolves in this. Accuracy and time are the two main objectives which can be obtained by modifying particle swarm optimization algorithm. The result of the algorithm is the sensor’s threshold, finest decision fusion rule and the choice of sensors. The algorithm is capable of selecting the finest configuration for a given requirement which consist of multiple objectives and is shown in the result.

Solution of various non linear optimization problems is the main requirement in the power system and is presented by Y. del Valle et.al [106]. The systematical method may suffer from slow convergence and the bother of dimensionality, heuristics-based swarm intelligence could be the resourceful alternative. To solve the large scale non linear optimization problems, Particle Swarm optimization (PSO) which is a part of Swarm Intelligence family is used. A detail concept of PSO and its variants is presented in the paper and it also presents a study on the power system applications which has benefited from the powerful nature of PSO as an optimization scheme. Every application the requirement of the technical details for applying PSO such as the type, particle formulation and the most effective fitness is discussed.

The chemo tactic (foraging) activity of E. coli bacteria and the biology and physics behind this is explained by K. M. Passino [107]. On how foraging should proceed the researchers explained the control system on E. coli and discuss the range of bacterial swarming and social foraging that dictates it. Authors next presented a computer program which imitates the distributed optimization process which is represented by the activity of social foraging. The researchers use it to a multiple function minimization problem and discuss its relationship with existing optimization algorithm in brief to illustrate its operation. The researchers closes the article with a discussion in brief about the use of bio mimicry of social foraging in the development of adaptive controllers and cooperative control plans for autonomous vehicles and for this they provide some fundamental ideas.

Hidden Markov model (HMM) which is the foremost technology in speech recognition is presented by R. Hassan et.al [108]. Optimization model problem parameter is of the main interest of the researchers in this area. An algorithm known as Baum-Welch (BW) algorithm is reliable and efficient and therefore this it is a popular estimation method however it is easily trappable. Between various optimization method Partial Swarm optimization (PSO) and genetic algorithm (GA) has gained considerable attention in recent times. They both uses different computational effort and methods to find a solution for a given objective function but their performance can be compared to each other. The comparison between the performance and application of these two for the HMM optimization in speech recognition has been presented in the paper. In context of the recognition performance the experimental result shows that PSO has an upper hand over GA.

The comparison between the performance of PSO and GA with a cost function that has an objective of minimizing the intra-cluster distance and optimizing the energy utilization of the network simultaneously is presented by N. M. A. Latiff et.al [109]. In addition to this, a comparison is made among the famous cluster-based protocols developed for LEACH (low energy adaptive based hierarchy), WSNs, LEACH-C, the later being an enhanced version of LEACH, and the conventional K-means clustering algorithm. The simulation result shows that the proposed protocol which uses PSO algorithm has higher efficiency that can achieve data delivery at the base station and a better network lifetime over its other comparatives.

To offer improved performance on selected standards several extensions in the evolutionary algorithms (EAs) and Particle Swarm Optimization (PSO) have been suggested and presented by J. Vesterstrom and R. Thomsen [110]. In recent time a research over Differential Evolution (DE) has shown improved performance in various real world applications. The researchers evaluated performance of DE, PSO, and EAs concerning their general applicability as numerical optimization technique. The comparison is performed over 34 widely used standards. DE generally outperforms all the other algorithms and is shown in the result but over tow noisy functions, EA outperforms both DE and PSO.

Sensor coverage problem has been presented by N. A. B. A. Aziz et.al [111]. It is a vital issue in the WSNs where the coverage rate ensures the quality of the service and it should be high. A new algorithm to optimize the sensor coverage is presented in the paper using Particle Swarm Optimization (PSO). PSO is used to find the final position of the sensor that gives the best coverage whereas; the VORONOI diagram is being used to find the fitness of the solution.

X. Wang, S. Wang, and J. J. Ma [112] presented the effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is usually supported by the virtual force (VF) algorithm. However, in the VF algorithm, the virtual force exerted by stationary sensor nodes will hinder the movement of mobile sensor nodes. Particle swarm optimization (PSO) is introduced as another dynamic deployment algorithm, but in this case the computation time required is the big bottleneck. This paper proposes a dynamic deployment algorithm which is named "virtual force directed co-evolutionary particle swarm optimization" (VFCPSO), since this algorithm combines the co-evolutionary particle swarm optimization (CPSO) with the VF algorithm, whereby multiple swarms to optimize different mechanism of the resultant vectors is used by the CPSO for dynamic deployment cooperatively and the velocity of each particle is updated according to not only the historical local and global optimal solutions, but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFCPSO is competent for dynamic deployment in WSNs and has better performance with respect to computation time and effectiveness than the VF, PSO and VFPSO algorithms.

T. P. Hong and G. N. Shiu [113] a two-tiered wireless sensor networks consisting of small sensor nodes, application nodes and base-stations is considered. An algorithm for multiple base stations under general power-consumption constraints has been proposed which is based on particle swarm optimization (PSO). The proposed approach can search for nearly optimal BS locations in heterogeneous sensor networks, where application nodes may own different data transmission rates, initial energies and parameter values. Experimental results also show the good performance of the proposed PSO approach and the effects of the parameters on the results.

A. Gopakumar and L. Jacob [114] proposed a novel and computationally efficient global optimization method based on swarm intelligence for locating nodes in a WSN environment. The mean squared range error of all neighboring anchor nodes is taken as the objective function for this non linear optimization problem. The Particle Swarm Optimization (PSO) is a high performance stochastic global optimization tool that ensures the minimization of the objective function, without being trapped into local optima. The easy implementation and low memory requirement features of PSO make it suitable for highly resource constrained WSN environments. Computational experiments on data drawn from simulated WSNs show better convergence characteristics than the existing Simulated Annealing based WSN localization.

K. S. Low et.al [115] a low cost localization system based on the measurements from a pedometer and communication ranging between neighboring nodes is presented. Unlike most of the existing methods that require good network connectivity, the proposed system works well in a sparse network. The localization information is obtained through a probability based algorithm that requires the solving of a nonlinear optimization problem. To obtain the optimum location of the sensor nodes, the particle swarm optimization (PSO) scheme that can be realized with a microcontroller for real time application is investigated in this paper. Experimental results show that the proposed approach yields good performance.

N. M. A. Latiff et.al [116] presented an energy-aware clustering for wireless sensor networks using particle swarm optimization (PSO) algorithm which is implemented at the base station. The researchers defined a new cost function, with the objective of simultaneously minimizing the intra-cluster distance and optimizing the energy consumption of the network. The performance of our protocol is compared with the well known cluster-based protocol developed for WSNs, LEACH (low-energy adaptive clustering hierarchy) and LEACH-C, the later being an improved version of LEACH. Simulation results demonstrate that our proposed protocol can achieve better network lifetime and data delivery at the base station over its comparatives.

T. Wimalajeewa and S. K. Jayaweera [117] assumed amplify-and-forward local processing at each node. The wireless link between sensors and the fusion center is assumed to undergo fading and coefficients are assumed to be available at the transmitting sensors. The objective is to minimize the total network power to achieve a desired fusion error probability at the fusion center. For i.i.d. observations, the optimal power allocation is derived analytically in closed form. When observations are correlated, first, an easy to optimize upper bound is derived for sufficiently small correlations and the power allocation scheme is derived accordingly. Next, an evolutionary computation technique based on particle swarm optimization is developed to find the optimal power allocation for arbitrary correlations. The optimal power scheduling scheme suggests that the sensors with poor observation quality and bad channels should be inactive to save the total power expenditure of the system. It is shown that the probability of fusion error performance based on the optimal power allocation scheme outperforms the uniform power allocation scheme especially when either the number of sensors is large or the local observation quality is good.