The Dynamic Source Routing Algorithm

Published Date: 02 Nov 2017

Lei Shu et.al [] used multimedia sensor nodes can significantly enhance the capability of wireless sensor networks (WSNs) for event description. In a number of scenarios, e.g., an erupting volcano, the WSNs are not deployed to work for an extremely long time. Instead, the WSNs aim to deliver continuous and reliable multimedia data as much as possible within an expected lifetime. In this paper, authors focus on the efficient gathering of multimedia data in WSNs within an expected lifetime. An adaptive scheme to dynamically adjust the transmission Radius and data generation Rate Adjustment (RRA) is proposed based on a cross layer design by considering the interaction among physical, network and transport layers. Designer first minimize the end-to-end transmission delay in WSNs while using the minimum data generation rate. In this phase, an optimal transmission radius can be derived. Then, using this transmission radius, they adaptively adjust the data generation rate to increase the amount of gathered data. Simulation results show that the proposed RRA strategy can effectively enhance the data gathering performance in wireless multimedia sensor networks (WMSNs) by dynamically adjusting the transmission radius of sensor nodes and the data generation rate of source nodes.

Jianping Yu et.al [] 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 [] 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.

Shengxiang Yang et.al [] presented in recent years, the static shortest path (SP) problem has been well addressed using intelligent optimization techniques e.g., artificial neural networks, genetic algorithms (GAs), particle swarm optimization, etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile networks [mobile ad hoc networks (MANETs)], wireless sensor networks, etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, i.e., the network topology changes over time due to energy conservation or node mobility. Therefore, the SP routing problem in MANETs turns out to be a dynamic optimization problem. Here authors propose to use GAs with immigrants and memory schemes to solve the dynamic SP routing problem in MANETs. Authors consider MANETs as target systems because they represent new-generation wireless networks. The experimental results show that these immigrants and memory-based GAs can quickly adapt to environmental changes (i.e., the network topology changes) and produce high-quality solutions after each change.

Akira Mutazono et.al [] presented one of the most challenging research tasks in the field of wireless sensor networks is controlling the power consumption of batteries and prolonging network lifetime. For sensor networks which consist of a large number of sensor nodes, self-organized control is more suitable than centralized control. In particular, research on bio-inspired self-organization methods attracts attention due to the potential applicability of such methods to wireless sensor networks. Here authors consider and focus on the calling behavior of Japanese tree frogs. These frogs display a type of behavior known as "satellite behavior", where a frog stops calling once it detects the calls of other neighboring frogs. This behavior can be applied in the design of an energy-efficient sleep control mechanism which provides adaptive operation periods. Authors propose a self organizing scheduling scheme inspired by the frogs' calling behavior for energy-efficient data transmission in wireless sensor networks. Simulation results show that the proposed sleep control method prolongs network lifetime by a factor of6.7 as compared with the method without sleep control for a coverage ratio of 80%.

Raghavendra V. Kulkarni, and Ganesh Kumar Venayagamoorthy [] presented wireless sensor networks (WSNs) are networks of autonomous nodes used for monitoring an environment. Developers of WSNs face challenges that arise from communication link failures, memory and computational constraints, and limited energy. Many issues in WSNs are formulated as multidimensional optimization problems, and approached through bio-inspired techniques. Particle swarm optimization (PSO) is a simple, effective and computationally efficient optimization algorithm. It has been applied to address WSN issues such as optimal deployment, node localization, clustering and data-aggregation. Here authors focused on issues in WSNs, introduces PSO and discusses its suitability for WSN applications. It also presents a brief survey of how PSO is tailored to address these issues.

Luiz Filipe M. et.al [] proposed a SEA Swarm (Sensor Equipped Aquatic Swarm) is a collection of mobile underwater sensors that moves as a group with water current and enables 4D (space and time) monitoring of local underwater events such as contaminants and intruders. For prompt alert reporting, mobile sensors routes events to mobile sinks (i.e., autonomous underwater vehicles) via geographic routing that is known to be most efficient under mobility and scarce acoustic bandwidth. In order for a packet to be routed to the destination using geographical routing, it requires to know the location of the destination. This is accomplished by having a location service that returns the location of a requested node. Here authors’ main goal is to design such location service for SEA Swarm, and also they analyze various design choices to realize an efficient location service in SEA Swarm scenarios. Authors find that conventional ad hoc network location service protocols cannot be directly used, because the entire swarm moves along water current. Authors prove that maintaining location information in a2D plane is a better design choice. Given this, authors propose a bio-inspired location service called a Phero-Trail location service protocol. In Phero-Trail, location information is stored in a 2Dupper hull of a SEA Swarm, and a mobile sink uses its trajectory(`a la a pheromone trail of ants) projected to the 2D hull to maintain location information. This enables mobile sensors to efficiently locate a mobile sink. The results show that Phero-Trail performs better than existing approaches.

Maumita Bandyopadhyay, and Parama Bhaumik [] presented ant colony optimization (ACO) is a stochastic approach for solving combinatorial optimization problems like routing in computer networks. The idea of this optimization is based on the food accumulation methodology of the ant community. Zone based routing algorithms is build on the concept of individual node’s position for routing of packets in mobile ad-hoc networks. Here the nodes’ position can be further utilized to discover routes by the Ants in optimized way. Position based routing algorithms (POSANT) had some significant loopholes to find route (source to destination) like it never guarantees the route would be the shortest one, in cases while it is able to find it. On the contrary, routing algorithms which are based on ant colony optimization find routing paths that are close in length to the shortest paths. The drawback of these algorithms is the large number of control messages that needs to be sent or the long delay before the routes are established from a source to a destination. Here authors have used Zone based ANT colony using Clustering which assures to find shortest route using the DIR principle (In this principle, the source or intermediate node transmits message to several neighbors and the node whose direction is closest to the direction of destination gets selected as the next hop forwarding node.) together with minimum overhead for route discovery and mobility management. Unlike other Zone based approach, in clustering it is not required to consider zone related information of each node while finding shortest path. Here, it is being proposed a new routing algorithm for mobile adhoc network by combining the concept of Ant Colony approach and Zone based routing approach using clustering to get shortest path with small number of control messages to minimize the overhead. Simulations show that Zone Based ant colony routing algorithm has relatively short route establishment overhead than other zone based ant colony algorithms in highly mobile scenarios.

Tanveer A Zia and Md Zahidul Islam [] deployment of wireless sensor networks in sensitive applications such as healthcare, defense, habitat monitoring and early bushfire detection requires a careful consideration. These networks are prone to security attacks due to their wireless and deployment nature. It is very likely that after deployment of the network, sensor nodes are left unattended which causes serious security concerns. Insecure wireless communication aggravates the inherent vulnerabilities of wireless sensor networks. Several countermeasures have been proposed in literature to counter the threats posed by attacks in sensor networks; however, security does not come for free. Especially for the resource limited nodes it is very costly to deploy computationally extensive security solutions. Here author’s studies the notion of trust in wireless sensor networks and proposes a solution based on communal reputation and individual trust (CRIT) in sensor nodes. A very important aspect which determines the viability of this study is the simulation results and performance analysis.

Changlei Liu and Guohong Cao [] 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.

Federico S. et.al [] presented flock of birds self-organize into V-formations when they need to travel long distances. It has been shown that this formation allows the birds to save energy, by taking advantage of the up wash generated by the neighboring birds. In this work authors use a model for the up wash generated by a flying bird, and show that a flock of birds can self-organize into a V-formation if every bird were to process spatial and network information through an adaptive diffusive process. The diffusion algorithm requires the birds to obtain measurements of the up wash, and also to use information from neighboring birds. The result has interesting implications. First, a simple diffusion algorithm can account for self-organization in birds. The algorithm is fully distributed and runs in real time. Second, according to the model, that birds can self-organize based on the up wash generated by the other birds. Third, that some form of information sharing among birds is necessary to achieve flight formation. They also propose a modification to the algorithm that allows birds to organize into a U-formation, starting from a V-formation. Authors show that this type of formation leads to an equalization effect, where every bird in the flock observes approximately the same up wash.

Mehmet E. Aydin et.al [] 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 [] 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 [] 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 [] 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.

Santosh Kulkarniand Prathima Agrawal [] presented wireless Sensor Networks have found wide acceptance in a range of user applications, due to their ability to monitor ambient conditions without human supervision. These networks typically consist of a large number of densely deployed, low-cost, low-power, multi-functional sensor nodes that interact with each other using wireless links. Despite several limitations in their individual resources, it is well known that, cooperation among sensor nodes can result in large and powerful ad-hoc computing systems. Towards this end, Authors consider some other characteristics, the storage capacity of individual nodes as it is usually very limited, compared to the amount of data that a node generates. Limited storage capability, coupled with temporal availability of sink nodes and varying importance of collected data mean that, the generated data needs to be stored locally, in the network, for later retrieval. To achieve this, authors introduce a cooperative storage mechanism that is based on the behavior of Honey Ants in the real world. The proposed technique is based on the concept of data migration and relies on migratory transportation units called Ant Agents to seek out spare storage resources within the network. Simulation results for the proposed model demonstrate that cooperative data accumulation is indeed beneficial for storage constrained nodes in wireless sensor networks.

K. Veeramachaneni and L. A. Osadciw [] presented a Swarm Intelligence based approach for sensor management of a multi sensor networks. Alternate sensor configurations and fusion strategies are evaluated by swarm agents, and an optimum configuration and fusion strategy evolves. An evolutionary algorithm, particle swarm optimization, is modified to optimize two objectives: accuracy and time. The output of the algorithm is the choice of sensors, individual sensor's thresholds and the optimal decision fusion rule. The results achieved show the capability of the algorithm in selecting optimal configuration for a given requirement consisting of multiple objectives.

Y. del Valle et.al [] presented many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.

K. M. Passino [] explained the biology and physics underlying the chemo tactic (foraging) behavior of E. coli bacteria. The researchers explained a variety of bacterial swarming and social foraging behaviors and discuss the control system on the E. coli that dictates how foraging should proceed. Next, a computer program that emulates the distributed optimization process represented by the activity of social bacterial foraging is presented. To illustrate its operation, the researchers applied it to a simple multiple-extremum function minimization problem and briefly discuss its relationship to some existing optimization algorithms. The article closes with a brief discussion on the potential uses of bio-mimicry of social foraging to develop adaptive controllers and cooperative control strategies for autonomous vehicles. For this, the researchers provided some basic ideas and invite the reader to explore the concepts further.

R. Hassan et.al [] presented Hidden Markov model (HMM) is the dominant technology in speech recognition. The problem of optimizing model parameters is of great interest to the researchers in this area. The Baum-Welch (BW) algorithm is a popular estimation method due to its reliability and efficiency. However, it is easily trapped in local optimum. Recently, genetic algorithm (GA) and particle swarm optimization (PSO) have attracted considerable attention among various modern heuristic optimization techniques. Since the two approaches are supposed to find a solution to a given objective function but employ different strategies and computational effort, it is appropriate to compare their performance. This paper presents the application and performance comparison of PSO and GA for continuous HMM optimization in continuous speech recognition. The experimental results demonstrate that PSO is superior to GA in respect of the recognition performance.

N. M. A. Latiff et.al [] presented performance comparison between particle swarm optimization (PSO) and genetic algorithms (GA) with a new cost function that has the objective of simultaneously minimizing the intra-cluster distance and optimizing the energy consumption of the network. Furthermore, a comparison is made with the well known cluster-based protocols developed for WSNs, LEACH (low-energy adaptive clustering hierarchy) and LEACH-C, the later being an improved version of LEACH, as well as the traditional K-means clustering algorithm. Simulation results demonstrate that the proposed protocol using PSO algorithm has higher efficiency and can achieve better network lifetime and data delivery at the base station over its comparatives.

J. Vesterstrom and R. Thomsen [] presented several extensions to evolutionary algorithms (EAs) and particle swarm optimization (PSO) have been suggested during the last decades offering improved performance on selected benchmark problems. Recently, another search heuristic termed differential evolution (DE) has shown superior performance in several real-world applications. In this paper, the researchers evaluated the performance of DE, PSO, and EAs regarding their general applicability as numerical optimization techniques. The comparison is performed on a suite of 34 widely used benchmark problems. The results from our study show that DE generally outperforms the other algorithms. However, on two noisy functions, both DE and PSO were outperformed by the EA.

N. A. B. A. Aziz et.al [] focus of this study is the sensor coverage problem. It is a crucial issue in wireless sensor networks (WSN), where a high coverage rate will ensure a high quality of service of the WSN. This paper proposes a new algorithm to optimize sensor coverage using particle swarm optimization (PSO). PSO is chosen to find the optimal position of the sensors that gives the best coverage and Voronoi diagram is used to evaluate the fitness of the solution.

X. Wang, S. Wang, and J. J. Ma [] 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 the CPSO uses multiple swarms to optimize different components of the solution vectors 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 [] a two-tiered wireless sensor networks consisting of small sensor nodes, application nodes and base-stations is considered. An algorithm based on particle swarm optimization (PSO) is proposed for multiple base stations under general power-consumption constraints. 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 [] 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 [] 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 [] 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 [] 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.