Genetic algorithm-based partial charging schedule of rechargeable sensor networks

The use of rechargeable sensors is a promising solution for wireless sensor networks. On this type of network, mobile charging vehicles (MC) are used for charging sensors using wireless energy transfer (WET) technology. In on-demand charging, a sensor transmits a charging request to the service station, and the MC visits the sensor to transfer energy. The key disadvantages of utilizing MC-based WET are its high energy expenditure rate due to mobility, long service time, and slow charging rate. Because of these reasons, sensors deplete their energy and become dead before the MC reaches the requesting nodes to recharge. We have adapted a genetic algorithm-based partial charging scheme to serve the charging requests. Our objective is to improve the survival ratio of the network. Using comprehensive simulations, we analyze the performance of our proposed method and compare it to two other existing approaches. The simulation results demonstrate that our proposed algorithm improves the survival ratio by up to 20 % by developing a dynamic energy threshold function for transmitting charging requests from the sensors and a partial charging schedule using a genetic algorithm.     

Charging utility maximization in wireless rechargeable sensor networks

Wireless energy transfer as a promising technology provides an alternative solution to prolong the lifetime of wireless rechargeable sensor networks (WRSNs). In this paper, we study replenishing energy on sensors in a WRSN to shorten energy expiration durations of sensors, by employing a mobile wireless charger to replenish sensors dynamically. We first formulate a novel sensor recharging problem with an objective of maximizing the charging utility of sensors, subject to the total traveling distance of the mobile charger per tour and the charging time window of each to-be-charged sensor. Due to the NP-hardness of the problem, we then propose an approximation algorithm with quasi-polynomial time complexity. In spite of the guaranteed performance ratio of the approximate solution, its time complexity is prohibitively high and may not be feasible in practice. Instead, we devise a fast yet scalable heuristic for the problem in response to dynamic energy consumption of sensors in the network. Furthermore, we also consider the online version of the problem where sensor replenishment is scheduled at every fixed time interval. We finally conduct extensive experiments by simulation to evaluate the performance of the proposed algorithms. Experimental results demonstrate that the proposed algorithms are very promising.     

An improved algorithm for dispatching the minimum number of electric charging vehicles for wireless sensor networks

The very limited sensor battery energy greatly hinders the large-scale, long-term deployments of wireless sensor networks. This paper studies the problem of scheduling the minimum charging vehicles to charge lifetime-critical sensors in a wireless rechargeable sensor network, by utilizing the breakthrough wireless charging technology. Existing studies still employ a number of charging vehicles to charge sensors. The purchase cost of a charging vehicle however is not inexpensive. To further reduce the number of employed charging vehicles, we propose a novel approximation algorithm, by exploring the combinatorial properties of the problem. The techniques exploited in this paper are essentially different from that in existing studies. Not only do we show that the approximation ratio of the proposed algorithm is much better than that of the state-of-the-art, but also extensive experimental results demonstrate that the number of scheduled charging vehicles by the proposed algorithm is at least 10% less than that by the existing algorithms and the total travel energy consumption of the charging vehicles is also smaller than that by the existing algorithms.

     

Increasing lifetime of wireless sensor networks using controllable mobile cluster heads

In a static wireless sensor network (WSN), sensors close to the base station (BS) run out of energy at a much faster rate than sensors in other parts of the network. This is because the sensor close to the BS always relays the data for other sensors, resulting in an unequal distribution of network residual energy. In this paper, we propose a scheme for enhancing the network lifetime using multiple mobile cluster heads (CHs) that can move in the WSN in a controllable manner. The CH controllably moves toward the energy‐rich sensors or the event area, offering the benefits of maintaining the remaining energy more evenly, or eliminating multihop transmission. Therefore, the proposed scheme increases the network lifetime. We theoretically analyze the energy consumption in our scheme and propose three heuristical mobility strategies. We further study the collaboration among CHs in order to maintain their connectivity to the BS to ensure the delay requirement for real‐time applications. Simulation shows that network lifetime is increased by upto 75% over existing approach by making CHs always move toward a stable equilibrium point. Our connectivity algorithm provides a best case improvement of 40% in transmission delays over existing schemes. Copyright © 2009 John Wiley & Sons, Ltd.     

Prolonging lifetime for wireless rechargeable sensor networks through sleeping and charging scheduling

Prolonging network lifetime is a fundamental requirement in wireless sensor network (WSN). Existing charging scheduling algorithms suffer from high node redundancy and energy consumption issues. In this paper, we study WSN charging problem from the perspectives of energy conservation combined with energy replenishment scheduling. Firstly, we detect the redundant nodes whose energy is wasted in the network functionality and develop a K‐covering redundant nodes sleeping scheduling algorithm (KRSS) for reducing energy. Secondly, we employed multiple wireless charging vehicles (WCVs) for energy replenishment and optimize the charging scheduling algorithm to prevent any exhaustion of nodes, and we proposed a distance and energy–oriented charging scheduling algorithm (DECS) with multiple WCVs. Simulation experiments are conducted to show the advantages of the proposed KRSS+DECS, confirming that our scheme is capable of removing redundant nodes, lowering node failures, and prolonging network lifetime.     

Deployment strategy for static chargers in WRSN

The traditional deployment strategy of static chargers in wireless rechargeable sensor networks (WRSN) covers all the area.The basic idea is to cover all the positions of nodes.A mathematical model of distance between a charger and the farthest node was established,the relationship between the number of nodes and the mathematical expectation of minimum radius of charging was analyzed,and deployment strategy for static chargers was proposed.The method based on the locations of all nodes that need to be charged in the area,used the smallest enclosing circle (SEC) algorithm and finds the optimal location of the charger through Euclidean delivery boy algorithm.It will decrease charging radius,reduce the minimum required transmitted power,thereby saving the average charging energy consumption.The experimental results demonstrated that the less the number locations that the nodes existed,the more energy will be saved.     

Demand‐based charging strategy for wireless rechargeable sensor networks

A wireless power transfer technique can solve the power capacity problem in wireless rechargeable sensor networks (WRSNs). The charging strategy is a widespread research problem. In this paper, we propose a demand‐based charging strategy (DBCS) for WRSNs. We improved the charging programming in four ways: clustering method, selecting to‐be‐charged nodes, charging path, and charging schedule. First, we proposed a multipoint improved K‐means (MIKmeans) clustering algorithm to balance the energy consumption, which can group nodes based on location, residual energy, and historical contribution. Second, the dynamic selection algorithm for charging nodes (DSACN) was proposed to select on‐demand charging nodes. Third, we designed simulated annealing based on performance and efficiency (SABPE) to optimize the charging path for a mobile charging vehicle (MCV) and reduce the charging time. Last, we proposed the DBCS to enhance the efficiency of the MCV. Simulations reveal that the strategy can achieve better performance in terms of reducing the charging path, thus increasing communication effectiveness and residual energy utility.     

Efficient motion vector prediction algorithm using pattern matching

The state-of-the-art median prediction scheme is widely used for predicting motion vectors (MVs) in recent video standards. By exploiting the spatial correlations among MVs, median prediction scheme predicts MV for current block from three neighboring blocks. When MV is obtained from motion estimation, MV difference (MVD) is calculated and then transmitted. This process for predicting MV and calculating MVD is known as MV coding process. For MV coding, the performance depends on how efficient both the spatial and the temporal correlations among MVs are being exploited. Median prediction scheme applies a sophisticated way including some special rules to exploit the spatial correlations, however the temporal correlations among successive MVs are not exploited. In this paper, a new algorithm named MV pattern matching (MV-PM) exploiting both the spatial and temporal correlations is proposed. Various kinds of experimental results show that the proposed MV-PM algorithm outperforms the median prediction and the other related prediction schemes.     

一种射频能量捕获网络移动能量源均衡化充电策略

为解决传统电池供电传感器网络存在的电池不易更换、节点能量容易耗尽等问题,射频能量捕获技术已逐步应用于无线可充电传感器网络中.由于不同位置传感器节点的工作负荷不同,捕获能量也有差异,实现节点能量的均衡化分布可以有效地提高节点的存活率.考虑射频能量源移动充电的场景,在已知节点位置信息的条件下,设计合理均衡的路由方案和充电算法.首先将区域基于蜂窝六边形网格划分,分别对网格和节点分层,提出逐层传输的均衡式路由策略,然后给出无线充电小车的移动路径,对相邻两层内节点剩余能量的方差最小化问题建模,由内层向外层依次确定能量源在各停留点的充电时间.仿真结果表明,相比已有的均衡化充电方法,该策略可以明显提高节点剩余能量的均衡性,从而延长网络的生命周期.     

A new load balancing and data collection algorithm for energy saving in wireless sensor networks

Data gathering is a major function of many applications in wireless sensor networks. The most important issue in designing a data gathering algorithm is how to save energy of sensor nodes while meeting the requirements of special applications or users. Wireless sensor networks are characterized by centralized data gathering, multi-hop communication and many to one traffic pattern. These three characteristics can lead to severe packet collision, network congestion and packet loss, and even result in hot-spots of energy consumption thus causing premature death of sensor nodes and entire network. In this paper, we propose a load balance data gathering algorithm that classifies sensor nodes into different layers according to their distance to sink node and furthermore, divides the sense zone into several clusters. Routing trees are established between sensor node and sink depending on the energy metric and communication cost. For saving energy consumption, the target of data aggregation scheme is adopted as well. Analysis and simulation results show that the algorithm we proposed provides more uniform energy consumption among sensor nodes and can prolong the lifetime of sensor networks.     

Energy equalizing routing for fast data gathering in wireless sensor networks

Energy saving and fast responding of data gathering are two crucial factors for the performance of wireless sensor networks. A dynamic tree based energy equalizing routing scheme (DTEER) was proposed to make an effort to gather data along with low energy consumption and low time delay. DTEER introduces a dynamic multi-hop route selecting scheme based on weight-value and height-value to form a dynamic tree and a mechanism similar to token passing to elect the root of the tree. DTEER can simply and rapidly organize all the nodes with low overhead and is robust enough to the topology changes. When compared with power-efficient gathering in sensor information systems (PEGASIS) and the hybrid, energy- efficient, distributed clustering approach (HEED), the simulation results show that DTEER achieves its intention of consuming less energy, equalizing the energy consumption of all the nodes, alleviating the data gathering delay, as well as extending the network lifetime perfectly.     

Energy-Efficient Routing Protocol for Wireless Sensor Networks with Static Clustering and Dynamic Structure

Data gathering is an essential operation in wireless sensor networks. For periodic data gathering applications, each sensor node has data that must be sent to a distant base station in a round of communication. Due to the limited battery power of sensor nodes, each sensor node transmitting its sensed data to the base station directly significantly consumes its energy. This work presents a hierarchical ring-based data gathering (HRDG) scheme for dense wireless sensor networks. A hierarchical grid structure is constructed, and only some sensor nodes are elected as grid heads for gathering data, subsequently reducing the total energy consumption per round. Grid heads are then organized into hierarchical rings to decrease the transmission delay of a round. The proposed HRDG scheme focuses on reducing the energy × delay cost in a round of data gathering. Moreover, the energy × delay cost of HRDG is analyzed. Simulation results indicate that the proposed HRDG scheme outperforms other data gathering schemes in terms of the number of rounds, the energy × delay cost and coverage ratio.     

用于无线传感网络的高能效移动数据收集方案

Energy conservation in Wireless Sensor Networks (WSNs) has always been a crucial issue and has received increased attention in the recent years. A transmission scheme for energy-constrained WSNs is proposed in this paper. The scheme, called MIHOP (MIMO and Multi-hop), combines cluster-based virtual MIMO and multi-hop technologies. The multi- hop mode is employed in transmitting data when the related sensors are located within a specific number of hops from the sink, and the virtual MIMO mode is used in transmitting data from the remaining sensor nodes. We compare the energy consumption of different transmission schemes and propose an algorithm for determining the optimal hop count in MIHOP. A controllable mobile sink that reduces the energy consumed in sensor transmission is also adopted for data collection. The theoretical analysis and the Monte Carlo simulation demonstrate that the proposed scheme significantly outperforms individual virtual MIMO, multi-hop technologies, and double-string networks in terms of energy conservation. The energy consumption levels under the MIHOP scheme are approximately 12.98%, 47.55% and 48.30% less than that under virtual MIMO schemes, multi-hop networks and double- string networks, respectively.     

Anycast tree-based routing in mobile wireless sensor networks with multiple sinks

A routing scheme for wireless sensor networks with mobile sensors and mobile multiple sinks is proposed and studied. The scheme is based on expanding ring search, anycast messaging and reactive mode with maintaining route state information in sensors. As a result of a successful routing request issued by the sensor, it becomes a member of a routing tree with some sink as a root. Anycast messaging is used only at the stage of establishing a path from a sensor to a sink. Replies from sinks are always forwarded in unicast mode. This considerably reduces network traffic and, as a result, energy consumption by sensors. To take into account routing conditions for network nodes in receiving messages from different directions, the receiving area of each node is assumed to consist of a number of sectors, considered as independent links with random change of link states in time. The proposed routing scheme was investigated with the use of a detailed simulation model, implemented in terms of a class of extended Petri nets. In simulation the following performance metrics were investigated versus time-to-live value: response ratio, relative network traffic and relative energy consumption. These metrics were considered for a number of combinations of parameters, such as the number of sinks, sensor availability and link availability. The results of simulation were compared with published characteristics of a similar model, in which sensors do not maintain any routing state information, and is proved to outperform it.     

DR-NAP: Data reduction strategy using neural adaptation phenomenon in wireless sensor networks

Internet of Things (IoT) has got significant popularity among the researchers' community as they have been applied in numerous application domains. Most of the IoT applications are implemented with the help of wireless sensor networks (WSNs). These WSNs use different sensor nodes with a limited battery power supply. Hence, the energy of the sensor node is considered as one of the primary constraints of WSN. Besides, data communication in WSN dissipates more energy than processing the data. In most WSNs applications, the sensed data generated from the same location sensor nodes are identical or time-series/periodical data. This redundant data transmission leads to more energy consumption. To reduce the energy consumption, a data reduction strategy using neural adaptation phenomenon (DR-NAP) has been proposed to decrease the communication energy in routing data to the BS in WSN. The neural adaptation phenomenon has been utilized for designing a simple data reduction scheme to decrease the amount of data transmitted. In this way, the sensor node energy is saved and the lifetime of the network is enhanced. The proposed approach has been implanted in the existing gravitational search algorithm (GSA)-based clustered routing for WSN. The sensed data are transmitted to CH and BS using DR-NAP. Real sensor data from the Intel Berkeley Research lab have been used for conducting the experiments. The experiment results show 47.82% and 51.96% of improvement in network lifetime when compared with GSA-based clustered routing and clustering scheme using Canada Geese Migration Principle (CS-CGMP) for routing, respectively.     

Nature inspired discrete firefly algorithm for optimal mobile data gathering in wireless sensor networks

Nowadays wireless sensor networks enhance the life of human beings by helping them through several applications like precision agriculture, health monitoring, landslide detection, pollution control, etc. The built-in sensors on a sensor node are used to measure the various events like temperature, vibration, gas emission, etc., in the remotely deployed unmanned environment. The limited energy constraint of the sensor node causes a huge impact on the lifetime of the deployed network. The data transmitted by each sensor node cause significant energy consumption and it has to be efficiently used to improve the lifetime of the network. The energy consumption can be reduced significantly by incorporating mobility on a sink node. Thus the mobile data gathering can result in reduced energy consumption among all sensor nodes while transmitting their data. A special mobile sink node named as the mobile data transporter (MDT) is introduced in this paper to collect the information from the sensor nodes by visiting each of them and finally it sends them to the base station. The Data collection by the MDT is formulated as a discrete optimization problem which is termed as a data gathering tour problem. To reduce the distance traveled by the MDT during its tour, a nature-inspired heuristic discrete firefly algorithm is proposed in this paper to optimally collect the data from the sensor nodes. The proposed algorithm computes an optimal order to visit the sensor nodes by the MDT to collect their data with minimal travel distance. The proposed algorithm is compared with tree-based data collection approaches and ant colony optimization approach. The results demonstrate that the proposed algorithm outperform other approaches minimizing the tour length under different scenarios.     

一种无线社区医疗监护网络的设计

临床护理中的很多医疗监护设备,需要实时采集病人的血压、心电、血氧和体温等指标,但这些设备一般存在体积大、耗电高、设备固定以及接线繁多等缺点。设计了一种部署在居民社区、小区的无线传感器网络,并提出一种MHDC-LEACH(改进的多跳分簇路由)算法。仿真结果表明,与传统的LEACH算法相比,该算法可减少网络中节点与基站通信的次数,均衡网络负载,优化数据传输路由,延长网络的生命周期,有效减少死亡节点个数。当网络覆盖区域增大时,最多可节约网络能量约20%。     

Software‐defined network‐enabled opportunistic offloading and charging scheme in multi‐unmanned aerial vehicle ecosystem

Unmanned aerial vehicles (UAVs) are autonomous fliers, which can play different roles in modern day applications. In one of the important role, UAVs can act as aerial data forwarding nodes for communication range enhancement in remote areas. UAVs form a web of drones, which can be geo‐distributed across a large area to serve various applications. However, the two major contradicting challenges with respect to multi‐UAV networks are channel congestion and flight time enhancement. The use of effective data transmission techniques to handle congestion can lead to higher battery dissipation, which in turn end up in the reduction in flight time. However, it is utmost necessity to provide an effective framework, which can provide a viable solution for handling congestion in multi‐UAV networks while enhancing the flight time of UAVs. To handle these issues, software‐defined network (SDN)–enabled opportunistic offloading and charging scheme (SOOCS) in multi‐UAV ecosystem is designed in this paper. In this scheme, an opportunistic offloading scheme is proposed, which uses an SDN‐based control model to handle congestion issues. Apart from this, an opportunistic energy‐charging scheme is designed, wherein the UAVS can either replenish their batteries using solar plates or they can wirelessly charge energy from charging points deployed at various geo‐distributed locations. The proposed scheme is evaluated using a simulation‐based study over the realistic deployment of charging points in Chandigarh City, India. The results obtained show the superiority of SOOCS over other variants of its category in terms of end‐to‐end delay, throughput, and hand‐over latency.     

Improving lifetime and network connections of 3D wireless sensor networks based on fuzzy clustering and particle swarm optimization

3D wireless sensor network (3D-WSN) has attracted significant interests in recent years due to its applications in various disciplinary fields such as target detection, object tracking, and security surveillance. An important problem in 3D WSN is the sensor energy optimization which determines a topology of sensors to prolong the network lifetime and energy expenditure. The existing methods for dealing with this matter namely low energy adaptive clustering hierarchy, LEACH-centralized, K-Means, single hop clustering and energy efficient protocol, hybrid-LEACH and fuzzy C-means organize the networks into clusters where non-cluster head nodes mainly carry out sensing tasks and send the information to the cluster head, while cluster head collect data from other nodes and send to the base station (BS). Although these algorithms reduce the total energy consumption of the network, they also create a large number of network disconnect which refers to the number of sensors that cannot connect to its cluster head and the number of cluster heads that cannot connect to the BS. In this paper, we propose a method based on fuzzy clustering and particle swarm optimization to handle this problem. Experimental validation on real 3D datasets indicates that the proposed method is better than the existing methods.     

An improved key distribution mechanism for large-scale hierarchical wireless sensor networks

Wireless sensor networks are often deployed in hostile environments and operated on an unattended mode. In order to protect the sensitive data and the sensor readings, secret keys should be used to encrypt the exchanged messages between communicating nodes. Due to their expensive energy consumption and hardware requirements, asymmetric key based cryptographies are not suitable for resource-constrained wireless sensors. Several symmetric-key pre-distribution protocols have been investigated recently to establish secure links between sensor nodes, but most of them are not scalable due to their linearly increased communication and key storage overheads. Furthermore, existing protocols cannot provide sufficient security when the number of compromised nodes exceeds a critical value. To address these limitations, we propose an improved key distribution mechanism for large-scale wireless sensor networks. Based on a hierarchical network model and bivariate polynomial-key generation mechanism, our scheme guarantees that two communicating parties can establish a unique pairwise key between them. Compared with existing protocols, our scheme can provide sufficient security no matter how many sensors are compromised. Fixed key storage overhead, full network connectivity, and low communication overhead can also be achieved by the proposed scheme.