Multiobjective optimization‐based DV‐hop localization using NSGA‐II algorithm for wireless sensor networks

Localization is one of the important requirements in wireless sensor networks for tracking and analyzing the sensor nodes. It helps in identifying the geographical area where an event occurred. The event information without its position information has no meaning. In range‐free localization techniques, DV‐hop is one of the main algorithm which estimates the position of nodes using distance vector algorithm. In this paper, a multiobjective DV‐hop localization based Non‐Sorting Genetic Algorithm‐II (NSGA‐II) is proposed in WSNs. Here, we consider six different single‐objective functions to make three multiobjective functions as the combination of two each. Localization techniques based on proposed multiobjective functions has been evaluated on the basis of average localization error and localization error variance. Simulation results demonstrate that the localization scheme based on proposed multiobjective functions can achieve good accuracy and efficiency as compared to state‐of‐the‐art single‐ and multiobjective GA DV‐hop localization scheme.     

Empirical analysis of the reliability of low‐rate wireless u‐healthcare monitoring applications

In this paper, we demonstrate an empirical analysis of the reliability of low‐rate wireless u‐healthcare monitoring applications. We have considered the performance analysis of the IEEE 802.15.4 low‐rate wireless technologies for u‐healthcare applications. For empirical measurement, we considered three scenarios in which the reliability features of the low‐rate wireless u‐healthcare monitoring applications have been measured: (i) distance between sensor nodes and base station; (ii) deployment of the number of sensor nodes in a network; and (iii) data transmission by different time intervals. The experimental results show that received data are used to calculate BER and analyze the performance according to the scenarios. The BER is affected when varying the distance between sensor node and base station, the number of nodes, and time interval. Copyright © 2011 John Wiley & Sons, Ltd.     

Multi‐objective optimization for channel allocation in mobile computing using NSGA‐II

Efficient channel allocation to mobile hosts aims to minimize the number of blocked hosts and is of utmost importance in a mobile computing network. Also, to achieve highly reliable data transmission, wireless mobile networks require efficient and reliable link connectivity regardless of terminal mobility, and thus reliable traffic performance. A mobile network consists of mobile nodes, base stations, links, etc. that are often prone to failure. The multi‐objective optimization problem (MOP), does not offer one best solution with respect to all the objectives. The aim is to determine the trade‐off surface, which is a set of non‐dominated solution points known as Pareto‐optimal. The two objectives addressed in this paper are to minimize the number of blocked hosts while maximizing the reliability of the data transmission. A multi‐objective optimization is carried out to optimize both objectives simultaneously. The elitist NSGA‐II (non‐dominated sorting genetic algorithm) has been used as an evolutionary optimization technique to solve this problem. A population of efficient solutions results when the termination condition is satisfied. Also the Pareto‐optimal fronts obtained provide a wide range of trade‐off operating conditions from which an appropriate operating point may be selected by the decision maker. The experimental results are presented and analyzed for overall evaluation of the performance of the proposed work. Further, comparison of the results with the two recent earlier models reveals that the proposed work performs better in serving mobile hosts as it caters to two objectives simultaneously. Copyright © 2010 John Wiley & Sons, Ltd.     

Range‐free intersecting chord‐based geometric localization scheme for wireless sensor networks

Recent advancement in wireless sensor network has contributed greatly to the emerging of low‐cost, low‐powered sensor nodes. Even though deployment of large‐scale wireless sensor network became easier, as the power consumption rate of individual sensor nodes is restricted to prolong the battery lifetime of sensor nodes, hence the heavy computation capability is also restricted. Localization of an individual sensor node in a large‐scale geographic area is an integral part of collecting information captured by the sensor network. The Global Positioning System (GPS) is one of the most popular methods of localization of mobile terminals; however, the use of this technology in wireless sensor node greatly depletes battery life. Therefore, a novel idea is coined to use few GPS‐enabled sensor nodes, also known as anchor nodes, in the wireless sensor network in a well‐distributed manner. Distances between anchor nodes are measured, and various localization techniques utilize this information. A novel localization scheme Intersecting Chord‐Based Geometric Localization Scheme (ICBGLS) is proposed here, which loosely follows geometric constraint‐based algorithm. Simulation of the proposed scheme is carried out for various communication ranges, beacon broadcasting interval, and anchor node traversal techniques using Omnet++ framework along with INET framework. The performance of the proposed algorithm (ICBGLS), Ssu scheme, Xiao scheme, and Geometric Constraint‐Based (GCB) scheme is evaluated, and the result shows the fact that the proposed algorithm outperforms the existing localization algorithms in terms of average localization error. The proposed algorithm is executed in a real‐time indoor environment using Arduino Uno R3 and shows a significant reduction in average localization time than GCB scheme and similar to that of the SSU scheme and Xiao scheme.     

Multi-Objective Evolutionary Algorithm Based on Decomposition for Energy Efficient Coverage in Wireless Sensor Networks

Wireless sensor networks (WSNs) have become a hot area of research in recent years due to the realization of their ability in myriad applications including military surveillance, facility monitoring, target detection, and health care applications. However, many WSN design problems involve tradeoffs between multiple conflicting optimization objectives such as coverage preservation and energy conservation. Many of the existing sensor network design approaches, however, generally focus on a single optimization objective. For example, while both energy conservation in a cluster-based WSNs and coverage-maintenance protocols have been extensively studied in the past, these have not been integrated in a multi-objective optimization manner. This paper employs a recently developed multi-objective optimization algorithm, the so-called multi-objective evolutionary algorithm based on decomposition (MOEA/D) to solve simultaneously the coverage preservation and energy conservation design problems in cluster-based WSNs. The performance of the proposed approach, in terms of coverage and network lifetime is compared with a state-of-the-art evolutionary approach called NSGA II. Under the same environments, simulation results on different network topologies reveal that MOEA/D provides a feasible approach for extending the network lifetime while preserving more coverage area.     

3D node deployment strategies prediction in wireless sensors network

ABSTRACT

3D Deployment plays a fundamental role in setting up efficient wireless sensor networks (WSNs) and IoT networks. In general, WSN are widely utilised in a set of real contexts such as monitoring smart houses and forest fires with parachuted sensors. This study focus on planned 3D deployment in which the sensor nodes must be accurately positioned at predetermined locations to optimise one or more design objectives under some given constraints. The purpose of planned deployment is to identify the type, the number, and the locations of nodes to optimise the coverage, the connectivity and the network lifetime. There have been a large number of studies that proposed algorithms resolving the premeditated problem of 3D deployment. The objective of this study is twofold. The first one is to present the complexity of 3D deployment and then detail the types of sensors, objectives, applications and recent research that concerns the strategy used to solve this problem. The second one is to present a comparative survey between the recent optimisation strategies solving the problem of 3D deployment in WSN. Based on our extensive review, we discuss the strengths and limitations of each proposed solution and compare them in terms of WSN design factors.     

UEEDA: Uniform and energy‐efficient deployment algorithm for wireless sensor networks

Efficient and accurate sensor deployment is a critical requirement for the development of wireless sensor networks. Recently, distributed energy‐efficient self‐deployment algorithms, such as the intelligent deployment and clustering algorithm (IDCA) and the distributed self‐spreading algorithm (DSSA), have been proposed to offer almost uniform distribution for sensor deployment by employing a synergistic combination of cluster structuring and a peer‐to‐peer deployment scheme. However, both DSSA and IDCA suffer from unnecessary movements that have arisen from an inappropriate design in partial force. To improve the performance of self‐deployment algorithms, a uniform and energy‐efficient deployment algorithm (UEEDA) is proposed in this paper. Simulation results demonstrate that the proposed UEEDA outperforms both DSSA and IDCA in terms of uniformity and algorithm convergence speed. Copyright © 2007 John Wiley & Sons, Ltd.     

Coverage‐aware connectivity‐constrained unattended sensor deployment in underwater acoustic sensor networks

Careful deployment of nodes in underwater acoustic sensor networks in a distributed manner with the goal of maximized coverage and guaranteed connectivity is a challenging problem because it is very difficult and costly to access the 3D underwater environment. This paper presents a novel algorithm for self‐deployment of nodes in underwater acoustic sensor networks assuming that the nodes are randomly dropped to the water surface and form a densely populated connected network at the water surface. The idea of the algorithm is based on calculating an optimized depth for each node in the network in such a way that the possible sensing coverage overlaps are minimized and the connectivity of final topology is guaranteed. The algorithm has three main phases. In the first phase, nodes are organized in a tree structure that is rooted at the surface station. In the second phase, the depths for all nodes are computed iteratively at surface station. In the final phase, the calculated depths are distributed to nodes so that the nodes start sinking. The performance of the proposed approach is validated through simulation. We observed that the proposed approach performs at least 10% better in terms of network coverage than contemporary schemes in the literature. Copyright © 2016 John Wiley & Sons, Ltd.     

A frequency‐aware data‐centric mechanism for wireless sensor networks

Wireless sensor networks (WSNs) are characterized by their low bandwidth, limited energy, and largely distributed deployment. To reduce the flooding overhead raised by transmitting query and data information, several data‐centric storage (DCS) mechanisms are proposed. However, the locations of these data‐centric nodes significantly impact the power consumption and efficiency for information queries and storage capabilities, especially in a multi‐sink environment. This paper proposes a novel dissemination approach, which is namely the dynamic data‐centric routing and storage mechanism (DDCRS), to dynamically determine locations of data‐centric nodes according to sink nodes' location and data collecting rate and automatically construct shared paths from data‐centric nodes to multiple sinks. To save the power consumption, the data‐centric node is changed when new sink nodes participate when the WSNs or some queries change their frequencies. The simulation results reveal that the proposed protocol outperforms existing protocols in terms of power conservation and power balancing. Copyright © 2009 John Wiley & Sons, Ltd.     

Connectivity constrained wireless sensor deployment using multiobjective evolutionary algorithms and fuzzy decision making

Deployment of sensor nodes is an important issue in designing sensor networks. The sensor nodes communicate with each other to transmit their data to a high energy communication node which acts as an interface between data processing unit and sensor nodes. Optimization of sensor node locations is essential to provide communication for a longer duration. An energy efficient sensor deployment based on multiobjective particle swarm optimization algorithm is proposed here and compared with that of non-dominated sorting genetic algorithm. During the process of optimization, sensor nodes move to form a fully connected network. The two objectives i.e. coverage and lifetime are taken into consideration. The optimization process results in a set of network layouts. A comparative study of the performance of the two algorithms is carried out using three performance metrics. The sensitivity analysis of different parameters is also carried out which shows that the multiobjective particle swarm optimization algorithm is a better candidate for solving the multiobjective problem of deploying the sensors. A fuzzy logic based strategy is also used to select the best compromised solution on the Pareto front.     

Location dependent key management in sensor networks without using deployment knowledge

In this paper we propose an approach for key management in sensor networks which takes the location of sensor nodes into consideration while deciding the keys to be deployed on each node. As a result, this approach not only reduces the number of keys that have to be stored on each sensor node but also provides for the containment of node compromise. Thus compromise of a node in a location affects the communications only around that location. This approach which we call as location dependent key management does not require any knowledge about the deployment of sensor nodes. The proposed scheme starts off with loading a single key on each sensor node prior to deployment. The actual keys are then derived from this single key once the sensor nodes are deployed. The proposed scheme allows for additions of sensor nodes to the network at any point in time. We study the proposed scheme using both analysis and simulations and point out the advantages.     

A Novel Coverage Improved Deployment Strategy for Wireless Sensor Network

Coverage of the bounded region gets importance in Wireless Sensor Network (WSN). Area coverage is based on effective surface coverage with a minimum number of sensor nodes. Most of the researchers contemplate the coverage region of interest as a square and manifest the radio ranges as a circle. The area of a circle is much higher than the area of a square because of the perimeter. To utilize the advantage of the circle, the coverage region of interest is presumed as a circle for sensor node deployment. This paper proposes a novel coverage improved disc shape deployment strategy. Comparative analysis has been observed between circle and square regions of interest based on the cumulative number of sensor nodes required to cover the entire region. A new strategy named as disc shape deployment strategy is also proposed. Traditional hexagon and strip-based deployment strategies are compared with the disc shape deployment strategy. The simulation result shows that the circle shape coverage region of interest extremely reduces the required number of sensor nodes. The proposed deployment strategy provides desirable coverage, and it requires few more sensor nodes than hexagon shape deployment strategy.

     

An energy‐efficient asynchronous wake‐up scheme for underwater acoustic sensor networks

In addition to the requirements of the terrestrial sensor network where performance metrics such as throughput and packet delivery delay are often emphasized, energy efficiency becomes an even more significant and challenging issue in underwater acoustic sensor networks, especially when long‐term deployment is required. In this paper, we tackle the problem of energy conservation in underwater acoustic sensor networks for long‐term marine monitoring applications. We propose an asynchronous wake‐up scheme based on combinatorial designs to minimize the working duty cycle of sensor nodes. We prove that network connectivity can be properly maintained using such a design even with a reduced duty cycle. We study the utilization ratio of the sink node and the scalability of the network using multiple sink nodes. Simulation results show that the proposed asynchronous wake‐up scheme can effectively reduce the energy consumption for idle listening and can outperform other cyclic difference set‐based wake‐up schemes. More significantly, high performance is achieved without sacrificing network connectivity. Copyright © 2015 John Wiley & Sons, Ltd.     

A lightweight,self‐adaptive lock gate designation scheme for data collection in long‐thin wireless sensor networks

Constrained by the physical environments, the long‐thin topology has recently been promoted for many practical deployments of wireless sensor networks (WSNs). In general, a long‐thin topology is composed of a number of long branches of sensor nodes, where along a branch each sensor node has only one potential parent node toward the sink node. Although data aggregation may alleviate excessive packet contention, the maximum payload size of a packet and the dynamically changing traffic loads may severely affect the amount of sensor readings that may be collected along a long branch of sensor nodes. In addition, many practical applications of long‐thin WSNs demand the exact sensor readings at each location along the deployment areas for monitoring and analysis purposes, so sensor readings may not be aggregated when they are collected. This paper proposes a lightweight, self‐adaptive scheme that designates multiple collection nodes, termed lock gates, along a long‐thin network to collect sensor readings sent from their respective upstream sensor nodes. The self‐adaptive lock gate designation scheme balances between the responsiveness and the congestion of data collection while mitigating the funneling effect. The scheme also dynamically adapts the designation of lock gates to accommodate the time‐varying sensor reading generation rates of different sensor nodes. A testbed of 100 Jennic sensor nodes is developed to demonstrate the effectiveness of the proposed lock gate designation scheme. Copyright © 2011 John Wiley & Sons, Ltd.     

一种高效覆盖的水下传感器网络部署策略

传感器节点的部署直接关系到水下传感器网络的成本和性能.考虑到传感器节点间具有很强的协同能力,该文提出一种基于检测融合的部署策略.采用Neyman-Pearson准则融合单元网格内所有传感器节点的检测信息,实现正方形和正三角形两种单元网格的高效覆盖,进而分别给出针对两种单元网格的监测区域网格划分方法,从而确定监测区域需要的传感器节点数量以及放置的具体位置.通过仿真实验验证了该部署策略的有效性.结果表明,与不采用检测融合时相比,降低了传感器节点冗余度.使用相同数量的传感器节点,新的部署策略能够在保证一定感知质量的基础之上获得更大的覆盖范围.     

Long‐range wireless sensor networks with transmit‐only nodes and software‐defined receivers

Wireless sensor networks for environmental monitoring and agricultural applications often face Long‐range requirements at low bit rates together with a large numbers of nodes. This paper presents the design and test of a novel wireless sensor network that combines a large radio range with very low power consumption and cost. Our asymmetric sensor network uses ultra‐low‐cost 40‐MHz transmitters and a sensitive software‐defined radio receiver with multi‐channel capability. Experimental radio range measurements in two different outdoor environments demonstrate a single‐hop range of up to 1.8 km. A theoretical model for radio propagation at 40 MHz in outdoor environments is proposed and validated with the experimental measurements. The reliability and fidelity of network communication over longer periods is evaluated with a deployment for distributed temperature measurements. Our results demonstrate the feasibility of the transmit‐only low‐frequency system design approach for future environmental sensor networks. Although there have been several papers proposing the theoretical benefits of this approach, to the best of our knowledge, this is the first paper to provide experimental validation of such claims. Copyright © 2011 John Wiley & Sons, Ltd.     

A cluster‐based fault‐tolerant routing protocol for wireless sensor networks

Energy conservation and fault tolerance are two critical issues in the deployment of wireless sensor networks (WSNs). Many cluster‐based fault‐tolerant routing protocols have been proposed for energy conservation and network lifetime maximization in WSNs. However, these protocols suffer from high frequency of re‐clustering as well as extra energy consumption to tolerate failures and consider only some very normal parameters to form clusters without any verification of the energy sufficiency for data routing. Therefore, this paper proposes a cluster‐based fault‐tolerant routing protocol referred as CFTR. This protocol allows higher energy nodes to become Cluster Heads (CHs) and operate multiple rounds to diminish the frequency of re‐clustering. Additionally, for the sake to get better energy efficiency and balancing, we introduce a cost function that considers during cluster formation energy cost from sensor node to CH, energy cost from CH to sink, and another significant parameter, namely, number of cluster members in previous round. Further, the proposed CFTR takes care of nodes, which have no CH in their communication range. Also, it introduces a routing algorithm in which the decision of next hop CH selection is based on a cost function conceived to select routes with sufficient energy for data transfer and distribute uniformly the overall data‐relaying load among the CHs. As well, a low‐overhead algorithm to tolerate the sudden failure of CHs is proposed. We perform extensive simulations on CFTR and compare their results with those of two recent existing protocols to demonstrate its superiority in terms of different metrics.     

Flower pollination algorithm‐based energy‐efficient stable clustering approach for WSNs

The advances in the size, cost of deployment, and user‐friendly interface of wireless sensor devices have given rise to many wireless sensor network (WSN) applications. WSNs need to use protocols for transmitting data samples from event regions to sink through minimum cost links. Clustering is a commonly used method of data aggregation in which nodes are organized into groups to reduce energy consumption. Nonetheless, cluster head (CH) has to bear an additional load in clustering protocols to organize different activities within the cluster. Proper CH selection and load balancing using efficient routing protocol is therefore a critical aspect for WSN's long‐term operation. In this paper, a threshold‐sensitive energy‐efficient cluster‐based routing protocol based on flower pollination algorithm (FPA) is proposed to extend the network's stability period. Using FPA, multihop communication between CHs and base station is used to achieve optimal link costs for load balancing distant CHs and energy minimization. Analysis and simulation results show that the proposed algorithm significantly outperforms competitive clustering algorithms in terms of energy consumption, stability period, and system lifetime.     

Multi‐target localization in wireless sensor networks: a compressive sampling‐based approach

This paper considers the problem of localizing a group of targets whose number is unknown by wireless sensor networks. At each time slot, to save energy and bandwidth resources, only part of sensor nodes are scheduled to activate to remain continuous monitoring of all the targets. The localization problem is formulated as a sparse vector recovery problem by utilizing the spatial sparsity of targets’ location. Specifically, each activated sensor records the RSS values of the signals received from the targets and sends the measurements to the sink node where a compressive sampling‐based localization algorithm is conducted to recover the number and locations of targets. We decompose the problem into two sub‐problems, namely, which sensor nodes to activate, and how to utilize the measurements. For the first subproblem, to reduce the effect of measurement noise, we propose an iterative activation algorithm to re‐assign the activation probability of each sensor by exploiting the previous estimate. For the second subproblem, to further improve the localization accuracy, a sequential recovery algorithm is proposed, which conducts compressive sampling on the least squares residual of the previous estimate such that all the previous estimate can be utilized. Under some mild assumptions, we provide the analytical performance bound of our algorithm, and the running time of proposed algorithm is given subsequently. Simulation results demonstrate the effectiveness of our algorithms.Copyright © 2013 John Wiley & Sons, Ltd.     

Combinatorial Design of Key Distribution Mechanisms for Wireless Sensor Networks

Secure communications in wireless sensor networks operating under adversarial conditions require providing pairwise (symmetric) keys to sensor nodes. In large scale deployment scenarios, there is no priory knowledge of post deployment network configuration since nodes may be randomly scattered over a hostile territory. Thus, shared keys must be distributed before deployment to provide each node a key-chain. For large sensor networks it is infeasible to store a unique key for all other nodes in the key-chain of a sensor node. Consequently, for secure communication either two nodes have a key in common in their key-chains and they have a wireless link between them, or there is a path, called key-path, among these two nodes where each pair of neighboring nodes on this path have a key in common. Length of the key-path is the key factor for efficiency of the design. This paper presents novel deterministic and hybrid approaches based on Combinatorial Design for deciding how many and which keys to assign to each key-chain before the sensor network deployment. In particular, Balanced Incomplete Block Designs (BIBD) and Generalized Quadrangles (GQ) are mapped to obtain efficient key distribution schemes. Performance and security properties of the proposed schemes are studied both analytically and computationally. Comparison to related work shows that the combinatorial approach produces better connectivity with smaller key-chain sizes