Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm

Due to the constrained energy and computational resources available to sensor nodes, the number of nodes deployed to cover the whole monitored area completely is often higher than if a deterministic procedure were used. Activating only the necessary number of sensor nodes at any particular moment is an efficient way to save the overall energy of the system. A novel coverage control scheme based on multi-objective genetic algorithm is proposed in this paper. The minimum number of sensors is selected in a densely deployed environment while preserving full coverage. As opposed to the binary detection sensor model in the previous work, a more precise detection model is applied in combination with the coverage control scheme. Simulation results show that our algorithm can achieve balanced performance on different types of detection sensor models while maintaining high coverage rate. With the same number of deployed sensors, our scheme compares favorably with the existing schemes.     

Periphery deployment for wireless sensor systems with guaranteed coverage percentage

With the availability of tiny wireless sensors, it is now possible to track moving objects by placing such sensors on the targets, collecting needed data, and transmitting sensed data back to the sink for storage and analysis. For applications such as vessel clearance surveillance, landslide detection, conveyer monitoring, and body gesture tracking, the motions of the targets are often confined to a certain region, such as the water way or the mountain slope. To collect the data from the wireless sensors, base stations are usually needed, which are deployed at fixed positions around the monitored region. Unfortunately, due to issues such as potential interference, high packaging and deployment cost, and low reliability, many such applications could only deploy the base stations on the periphery of the monitored region. The question is how to deploy the base stations on the periphery so that they can cover the most area inside the monitored area. We formulate the periphery deployment problem and analyze the performance bound in terms of coverage percentage under both ideal and practical deployment conditions. Then, we describe a deployment procedure to solve the periphery deployment problem in polynomial time. The proposed algorithms are evaluated through extensive simulations drawn from a watercourse monitoring system. The results show that the proposed algorithms can reduce the size of the deployment set by 17% compared to the traditional area-coverage algorithms, and the coverage percentage is improved by 1.18 times.     

Data aggregation in wireless sensor networks using ant colony algorithm

Data aggregation is important in energy constraint wireless sensor networks which exploits correlated sensing data and aggregates at the intermediate nodes to reduce the number of messages exchanged network. This paper considers the problem of constructing data aggregation tree in a wireless sensor network for a group of source nodes to send sensory data to a single sink node. The ant colony system provides a natural and intrinsic way of exploring search space in determining data aggregation. Moreover, we propose an ant colony algorithm for data aggregation in wireless sensor networks. Every ant will explore all possible paths from the source node to the sink node. The data aggregation tree is constructed by the accumulated pheromone. Simulations have shown that our algorithm can reduce significant energy costs.     

无线传感器网络节点自调度冗余覆盖算法

对于能量受限的无线传感器网络,延长网络存活的时间很关键。针对这个问题,提出了一种基于能量均衡的传感器节点自调度冗余覆盖协议（SRCP）,通过仿真实验对该算法的有关性能进行了评价,性能评价表明：这种算法能有效使用节点能力,延长网络存活时间。     

Recovery from multiple simultaneous failures in wireless sensor networks using minimum Steiner tree

In some applications, wireless sensor networks (WSNs) operate in very harsh environments and nodes become subject to increased risk of damage. Sometimes a WSN suffers from the simultaneous failure of multiple sensors and gets partitioned into disjoint segments. Restoring network connectivity in such a case is crucial in order to avoid negative effects on the application. Given that WSNs often operate unattended in remote areas, the recovery should be autonomous. This paper promotes an effective strategy for restoring the connectivity among these segments by populating the least number of relay nodes. Finding the optimal count and position of relay nodes is NP-hard and heuristics are thus pursued. We propose a Distributed algorithm for Optimized Relay node placement using Minimum Steiner tree (DORMS). Since in autonomously operating WSNs it is infeasible to perform a network-wide analysis to diagnose where segments are located, DORMS moves relay nodes from each segment toward the center of the deployment area. As soon as those relays become in range of each other, the partitioned segments resume operation. DORMS further model such initial inter-segment topology as Steiner tree in order to minimize the count of required relays. Disengaged relays can return to their respective segments to resume their pre-failure duties. We analyze DORMS mathematically and explain the beneficial aspects of the resulting topology with respect to connectivity, and traffic balance. The performance of DORMS is validated through extensive simulation experiments.     

一种无线传感器网络四边测距定位算法

根据RSSI测距特性,在三边测距定位的基础上,提出了基于RSSI值测距的四边测距定位算法,突破了三边测距定位的局限性。为进一步提高精度,采用了求质心和加权质心的方法,并用C++实现仿真比较,仿真结果表明:加权质心算法具有较高的精度,整个定位过程中节点间无需额外的通信开销,具有广泛的实用性。     

拥塞敏感的无线传感器网络路由算法

如何延长无线传感器网络的生命周期是一个重要的挑战.提出了一种新的拥塞敏感的路由算法,该算法充分考虑了路由选择过程中的数据负载与邻节点的缓存、信道竞争等状态的关系,每个节点以此计算一个能反映本地拥塞状态的权值,节点选择权值最高的下游节点作为下一跳来平衡下游节点的能耗和数据负载,同时降低下游节点的拥塞可能性.模拟实验表明:...     

无线传感器网络中一种能量高效的分布式分簇算法

提出了一种适用于无线传感器网络的能量高效的分布式分簇算法（ＥＥＤＣ）,该算法使具有较高剩余能量及距离基站较近的节点有更大的机会成为簇头．理论分析表明该算法通信开销较小,而且有效地均衡了节点的能量消耗．为了确保ＥＥＤＣ 的正确性、完整性和可靠性,利用形式化方法———着色网对其关键属性进行建模和分析．仿真结果表明,ＥＥＤＣ 有效地延长了网络生命周期,提高了网络的能耗效率．     

Routing in wireless sensor networks for wind turbine monitoring

Smart wireless sensor devices are rapidly emerging as key enablers of the next evolution in wind turbine monitoring. The potential for in-situ monitoring of turbine elements, employing methodologies that are not possible with existing wired technology, make it possible to attain new levels of granularity and autonomy in the monitoring of these structures. Wireless sensor devices are limited in terms of communication by the range of their radio modules and, thus, need to form networks in order to transfer data from distant points. Routing protocols are primary enablers of such ad hoc wireless sensor networks and these require the implementation of reliable and energy-efficient mechanisms to maximize network reliability and availability. Existing routing protocols cannot be directly applied to the monitoring of wind turbines without addressing the unique context and operational characteristics of these structures in multi-hop wireless communication. This work identifies the potential effects associated with the operation, environment and structure of wind turbines in wireless sensor network multi-hop communication, and proposes and evaluates a reliable routing protocol for wireless sensor networks employed in these domains.     

An agent-assisted QoS-based routing algorithm for wireless sensor networks

Existing routing algorithms are not effective in supporting the dynamic characteristics of wireless sensor networks (WSNs) and cannot ensure sufficient quality of service in WSN applications. This paper proposes a novel agent-assisted QoS-based routing algorithm for wireless sensor networks. In the proposed algorithm, the synthetic QoS of WSNs is chosen as the adaptive value of a Particle Swarm Optimization algorithm to improve the overall performance of network. Intelligent software agents are used to monitor changes in network topology, network communication flow, and each node's routing state. These agents can then participate in network routing and network maintenance. Experiment results show that the proposed algorithm can ensure better quality of service in wireless sensor networks compared with traditional algorithms.     

Pareto-based evolutionary computational approach for wireless sensor placement

Wireless sensor networks (WSNs) have become increasingly appealing in recent years for the purpose of data acquisition, surveillance, event monitoring, etc. Optimal positioning of wireless sensor nodes is an important issue for small networks of relatively expensive sensing devices. For such networks, the placement problem requires that multiple objectives be met. These objectives are usually conflicting, e.g. achieving maximum coverage and maximum connectivity while minimizing the network energy cost. A flexible algorithm for sensor placement (FLEX) is presented that uses an evolutionary computational approach to solve this multiobjective sensor placement optimization problem when the number of sensor nodes is not fixed and the maximum number of nodes is not known a priori. FLEX starts with an initial population of simple WSNs and complexifies their topologies over generations. It keeps track of new genes through historical markings, which are used in later generations to assess two networks’ compatibility and also to align genes during crossover. It uses Pareto-dominance to approach Pareto-optimal layouts with respect to the objectives. Speciation is employed to aid the survival of gene innovations and facilitate networks to compete with similar networks. Elitism ensures that the best solutions are carried over to the next generation. The flexibility of the algorithm is illustrated by solving the device/node placement problem for different applications like facility surveillance, coverage with and without obstacles, preferential surveillance, and forming a clustering hierarchy.     

自适应半径调整的无线传感器网络覆盖算法

针对随机分布的无线传感器网络中节点分布不均匀造成的覆盖冗余,以及同时存在的覆盖空洞,提出了一种自适应半径调整无线传感器网络覆盖算法,通过阈值判断监测区域内传感器节点密度,根据监测区域内传感器节点疏密程度,利用节点半径步长系数对监测区域内节点半径进行自适应调整,建立无线传感器节点发射功率与节点发射半径的模型,计算无线传感器发射功率,通过实验和仿真,表明上述方法能够保证网络覆盖率的基础上减少无线传感器网络总功耗,提高网络寿命.     

WSN中基于端到端的贪婪故障定位算法

为了维持无线传感器网络的正常运行,所有的故障链路需要被精确定位。将该问题转换为基于端到端的数据引导,以减少主动监测次数为目的的最优监测序列的问题。提出了通过拓扑拆分得到故障子图,并通过子图的概率集进一步计算节省主动探测次数的基于节点监测多条链路的启发式贪婪算法NTHG(node testing using heuristic greedy)。仿真结果表明仅需要监测小部分的节点,就可以定位网络中所有的故障链路。与该问题最新的解决算法LTHG(link testing using heristic greedy)相比,新算法需要更少的监测次数和平均CPU耗时,从而很好地降低了网络能耗,缩短了故障定位耗时。     

基于蚁群的无线传感器网络路由算法

针对无线传感器节点能量、通信能力及计算能力有限等特点,将蚁群算法应用于无线传感器网络,提出一种改进的蚁群路由算法,考虑了节点的能量、距离、通信半径和传输方向等参数.实验结果表明:该算法有效地减少了网络能量消耗、节点死亡数、路由跳数和数据传输的路径长度,延长了无线传感器网络的寿命,实现无线传感器网络在通信过程中快速、节能的路由。     

A novel differential evolution based clustering algorithm for wireless sensor networks

Clustering is an efficient topology control method which balances the traffic load of the sensor nodes and improves the overall scalability and the life time of the wireless sensor networks (WSNs). However, in a cluster based WSN, the cluster heads (CHs) consume more energy due to extra work load of receiving the sensed data, data aggregation and transmission of aggregated data to the base station. Moreover, improper formation of clusters can make some CHs overloaded with high number of sensor nodes. This overload may lead to quick death of the CHs and thus partitions the network and thereby degrade the overall performance of the WSN. It is worthwhile to note that the computational complexity of finding optimum cluster for a large scale WSN is very high by a brute force approach. In this paper, we propose a novel differential evolution (DE) based clustering algorithm for WSNs to prolong lifetime of the network by preventing faster death of the highly loaded CHs. We incorporate a local improvement phase to the traditional DE for faster convergence and better performance of our proposed algorithm. We perform extensive simulation of the proposed algorithm. The experimental results demonstrate the efficiency of the proposed algorithm.     

一种能量高效的无线传感器网络分簇路由算法

无线传感器网络中节点的能量有限,提高能量的有效性便成为无线传感器网络路由协议设计的首要目标。设计了一种能量高效的分簇路由算法,它提出让候选节点在一定的覆盖范围内以剩余能量为标准来竞选簇头,以使簇头分布均匀;处于簇类交界的节点则根据能量和距离来选择归属的簇头,以平衡网络负载;新算法还采用多跳的簇间通信方式来降低大部分簇头节点的通信负载。仿真结果表明:新算法能够有效降低网络能耗,延长网络生存时间。     

无线传感器网络中基于分层的非均衡分簇算法

降低网络能量消耗、延长网络寿命是无线传感器网络设计的重要目标,分簇是实现该目标的主要方法之一。针对矩形传感器网络,提出一种基于分层的非均衡分簇算法。算法根据节点的能量消耗情况计算出了每层的宽度,可以有效实现网络的能耗均衡。将非均衡分簇算法与LEACH协议进行了比较,仿真实验表明:基于分层的非均衡分簇算法可以更好地实现能耗均衡,能有效延长网络的寿命。     

无线传感器网络LEACH协议的研究与改进

在分析了无线传感器网络中分层路由LEACH算法的基础上,提出了一种新型选择簇首节点的方法,并通过matlab对改进后的LEACH算法进行了仿真,仿真结果显示改进后的LEACH算法在网络生命周期和网络能量消耗两方面比LEACH算法有很大的提高。