Performance evaluation of sensor deployment using optimization techniques and scheduling approach for K-coverage in WSNs

Sensor deployment is a fundamental issue in Wireless Sensor Networks (WSNs). Studies on the coverage problem are concentrated on deploying sensors to cover determined areas or a set of determined points (targets). On the other hand, the sensor network should have a lifetime long enough to fulfill the application requirements. Therefore, prolonging the network lifetime must be taken into account in designing a solution for a WSN that covers determined points. The problem of determining the location of sensor nodes such that the targets are covered and the network lifetime is maximized is called the sensor deployment problem. So far, various approaches, such as heuristics and approximation algorithms, have been used to solve the sensor deployment problem in WSNs. In this paper, we use two improved versions of the Particle Swarm Optimization (PSO) algorithm to solve the sensor deployment problem. The first one is cooperative PSO and the second one is improved cooperative PSO using fuzzy logic. The simulation results show that the algorithms proposed in this paper solve the target coverage problem with a longer network lifetime compared to the other similar algorithms.

Hybrid meta-heuristic optimization based energy efficient protocol for wireless sensor networks

Coverage control is one of the key problems of research and application of wireless sensor networks. In this paper, the coverage control problem for hybrid wireless sensor network consisting of both static and mobile sensors is investigated. A dynamic repair mechanism based on the ant colony algorithm is proposed for coverage blind spots found in the lifetime of wireless sensor networks. Simulation results show that the proposed method not only can make the nodes deployment more even, but also can improve the network quality of service, which verifies the effectiveness and feasibility of the restoration mechanism based on ant colony algorithm.

Node scheduling in wireless sensor networks (WSNs) plays a vital role in conserving energy and lengthening the lifetime of networks, which are considered as prime design challenges. In large-scaled WSNs, especially where sensor nodes are deployed randomly, 100 % coverage is not possible all the times. Additionally, several types of applications of WSNs do not require 100 % coverage. Following these facts, in this paper, we propose a coverage based node scheduling algorithm. The algorithm shows that by sacrificing a little amount of coverage, a huge amount of energy can be saved. This, in turns, helps to increase the lifetime of the network. We provide mathematical analysis, which verifies the correctness of the proposed algorithm. The proposed algorithm ensures balanced energy consumption over the sensor networks. Moreover, simulation results demonstrate that the proposed algorithm almost doubles the lifetime of a wireless sensor network by sacrificing only 5–8 % of coverage.

Wireless networks play a crucial role in the communication systems nowadays. Wireless networks are being increasingly used in the communication among devices of the most varied types and sizes. User mobility, affordability, flexibility and ease of use are few of many reasons for making them very appealing to new applications and more users everyday. A Wireless Sensor Network (WSN) is composed of sensor nodes spread over the field to sense the data. The sensed data must be gathered & transmitted to Base Station (BS) for end user queries. The used sensor nodes being in- expensive having low computation power & limited energy so are not as much reliable as their expensive macro sensor counter parts but their size and cost enable hundred to thousand of micro sensors to achieve high quality fault tolerant system. In an environment where in each round all sensor nodes have to send data to base station; it is required to effectively utilize energy of sensor nodes so as to increase the life- time of the system. The use of data aggregation & fusion as proposed in LEACH increases system lifetime by a factor of 8 as compared to conventional routing protocols. In this work, our main focus is the static sensors are randomly selected and the base stations have their information all a priori. Basically, the sensors are in direct communication range of each other and can transmit to and receive from the base station. The nodes periodically sense the environment and have always data to send in each round of communication. The nodes fuse/ aggregate the data they receive from the others with their own data, and produce only one packet regardless of how many packets they receive. The problem is to find a routing scheme or an efficient protocol to deliver data packets collected from sensor nodes to the base station. It maximizes the lifetime of the sensor network under the system model given above. However, the definition of quality of service of the sensor network provides is not specified. Secondly, where the nodes are densely deployed, the quality of the system is affected as soon as a significant amount of nodes die, since adjacent nodes record identical or related data. In this case, the lifetime of the network is the time elapsed until half of the nodes or some specified portion of the nodes die. In general terms, the time in rounds where the last node depletes all of its energy defines the lifetime of the overall sensor network. Taking these different possible requirements under consideration, our work provides a proper timing of all deaths for all algorithms in detail as well as chooses the shortest possible path for communication with better memory management scheme and leaves the decision which one to choose to system designers.

The sensor relocation problem (SR problem) is an important issue in wireless sensor networks (WSNs). The main difference between SR problem and sensor deployment (SD) problem is that SR problem has a strict response time requirement. Because the SD problem occurs during network initialization, so there is no strict response time requirement. The SD problem is solved as long as the required coverage level is met. However, the SR problem occurs during operation of WSNs, we need to minimize the impact on the surveillance task while replacing faulty sensors with redundant sensors. In this paper, we revisit the SR problem in mobile WSNs. In terms of response time and moving distance, the proposed algorithm moves redundant sensors to faulty sensors and coverage holes by cascaded movement. It also considers the estimated value of network lifetime in the planning of cascading schedule. Hence, the SR problems caused by inappropriate cascading schedules can be avoided. Overall, the proposed SR algorithm has the following features: 1) shorter response time; 2) shorter moving distances; 3) smaller number of requesting places; and 4) longer network lifetime. The performance evaluation done in this paper has confirmed its superior performances in all these aspects.

A large number of sensor nodes are deployed to remote locations and left unattended for a long time in wireless sensor network applications. When a battery-powered sensor node depletes its energy, it is assumed as “dead” and recharging or replacing the battery is not possible in most of the applications. Lifetime of the wireless sensor network depends on the efficient use of available restricted energy. Railway monitoring application of Linear Wireless Sensor Networks (LWSNs) which are a subclass of wireless sensor networks is considered in this paper. The objective of this study is to present the effect of proper node deployment in extending the linear wireless sensor network lifetime. Thanks to proposed deployment scheme, it is shown that the energy consumption of the LWSN is lowered and thus the lifetime of the network is increased.

Wireless sensor networks (WSNs) function with constraints in energy, computation and storage. The real-time monitored sensor data are transmitted to the base station through intermediate nodes. Application-specific protocols are being developed to enhance the functionality and performance of WSNs. WSN topology is classified into three phases: (i) topology construction; (ii) topology control and (iii) topology maintenance. The authors propose Swarm Intelligence (SI)-based topology maintenance for link failure and congestion control in WSN. SI-based models like Particle Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO) are used in topology maintenance. The proposed model is compared with the existing distributed topology control techniques based on neighbour, location and direction attributes for WSNs. SI-based techniques indicate performance improvement in topology as compared to the existing techniques. The proposed SI model for topology maintenance evaluates WSN attributes based on (i) particle position and particle velocity in PSO; (ii) pheromone cost and (iii) forage success rate in ACO. SI-based topology maintenance is based on finding global and local minima during the search process. Simulation results indicate performance improvement in throughput, data transmission rate and average power efficiency in SI techniques as compared to the existing topology maintenance techniques.

Extensive research is being done on Wireless sensor Networks (WSNs) in present decade. WSNs consist of large number of sensor nodes which perform sensing of certain phenomena and send the sensed data to the sinks. This operation consumes energy of sensor nodes which effects the lifetime of a network. To prolong the lifetime of WSNs various strategies and routing algorithms have been proposed. Use of mobile sinks has proved effective in this respect. In this paper we are analyzing the impact of multiple mobile sinks with proactive hierarchical clustering protocols LEACH and network reactive hierarchical protocol TEEN. This paper will give simulation based analysis of using multiple mobile sinks in network and their effect on lifetime of a network. Performance analysis will be done on basis of dead nodes, alive nodes, throughput and residual energy.

The various sensor nodes in a wireless sensor network (WSN) have restricted energy resources which severely affect the long time performance of the network. So, the current research focus is to design energy efficient algorithms for WSNs for improving network lifetime. This paper proposes artificial bee colony (ABC) algorithm with a clustering model to improve the energy capability of the network. The ABC algorithm can improve the internal dynamics of the cluster head nodes and sensor nodes in the WSN. The proposed algorithm can reduce the energy dissipation of nodes, balance the energy consumption across nodes and maximize the lifetime of the network. The ABC algorithm has less number of control parameters in the objective function compared to other algorithms, so it is simple to implement in clustered sensor network. The simulation results prove the superiority of the ABC algorithm compared to other algorithms in maximizing the energy efficiency and longevity of the network.

Gbest-guided artificial bee colony algorithm for numerical function optimization

Proposing a centralized algorithm to minimize message broadcasting energy in wireless sensor networks using directional antennas

Sensor technology is developing rapidly and up to date. The lifetime of the Wireless Sensor Network (WSN) has also attracted many researchers, and the location of the Base Station (BS) plays a crucial role in prolonging the lifetime. The energy consumption in the WSN occurs during transmission of data and selection of cluster-head nodes. A reasonable location of the BS prolongs the lifetime of the WSN. This study proposes a Levy Flight Edge Regeneration Black Hole algorithm (LEBH) to speed up convergence and enhance optimization capabilities. The performance of LEBH and other heuristic algorithms are compared on CEC 2013. The result shows that the LEBH outperforms other heuristics in most cases. In this study, the energy consumption and WSN models are simulated. Subsequently, the proposed LEBH is combined with relocation technology to change the location of the BS to prolong the lifetime. Simulation results show LEBH-BS prolongs the lifetime of the WSN better than random-base and static-base stations and other heuristic algorithms in most cases.

Two kinds of sensors will be discussed in some sort of wireless sensor networks (WSNs). One is named a normal sensor (called A_nodes) with fixed initial energy, which can get perception data from the surrounding environment and have functions of storage and forwarding. The other is named relay sensor (called B_node) with sufficient energy, which only can store data and forward data. Cluster heads (called A_heads) are chosen among A_nodes by probability mode to create clusters first. Local adjustments would be done inter-cluster. Then the sink is selected as the root and a backbone shortest path tree with hops limited is built dynamically from A_heads, B_Nodes and sink. Inside the backbone shortest path aggregation tree, two steps are done. The first step, select some B_node as cluster head (denoted as B_node) and make local adjustment intra-clusters if it is possible. The second step, adjust the shortest path aggregation tree. By using both particle swarm optimization (PSO) and ant colony optimization (ACO), a traveling salesman problem (TSP) cycle with shorter path length is obtained (this method is noted as TSP_PSO_ACO). This TSP cycle consists of all nodes in the aggregation backbone tree. Along this cycle, energy is offered to nodes except sink. The above strategy would be discussed for its feasibility and time complexity. The experimental results imply that the energy consumption is reduced by using local adjustment. The relative failure rate of nodes can be reduced in the later stage of WSN survival. Offering energy to a small number of nodes several times has no obvious impact on the overall energy consumption and survival time of WSN. The case with some small-sized obstacles in WSN area is also discussed. There are little affections on TSP cycles both from results on theoretical analysis and simulation if there are some obstacles with small size in WSN area.

A wireless sensor network (WSN) comprised of a plurality of sensor nodes powered by their internal batteries and one or more base stations. Efficient utilization of the energy resources leads to prolonged network lifetime. In this study, the effects of limiting the total hop count within the network on network lifetime is investigated by using a mixed integer programming (MIP) framework. Our results show that when the hop count of network is limited by minimum hop count, lifetime of network can decrease by %40 of the lifetime obtained without any constraint on hop count. If hop count limit of network is increased by %10, lifetime of network can reach %98 of maximum lifetime obtained without any restrictions on hop count.