Connected K-target coverage problem in wireless sensor networks with different observation scenarios

In this paper, we consider the problem of scheduling sensor activities to maximize network lifetime while maintaining both discrete K-target coverage and network connectivity. In K-target coverage, it is required that each target should be simultaneously observed by at least K sensors. The data generated by the sensors will be transmitted to the sink node via single or multiple hop communications. As maintaining discrete target coverage cannot guarantee the network connectivity, we consider both target coverage and connectivity issues. Further, by adopting a more realistic energy consumption model, we consider the sensor activity scheduling problem and routing problem jointly. We study the problem with two observation scenarios depending on whether a sensor can distinguish the targets in its sensing area or not. For the first scenario, a more general scenario where each sensor can simultaneously observe multiple targets is considered and we develop a polynomial-time algorithm which can achieve optimal solution based on linear programming and integer theorem. For the second scenario, we show that the problem is NP-complete and develop an approximation algorithm for solving it. As the protocol cost of the optimal solution and the approximation algorithm may be high in practice, we develop a low-cost heuristic algorithm which can be implemented in a distributed fashion for both scenarios. We demonstrate the effectiveness of the heuristic algorithm through extensive simulations.     

A Lagrangean relaxation based sensor deployment algorithm to optimize quality of service for target positioning

The target positioning service is one of useful applications for wireless sensor networks. So far, most papers considered traditional uniform quality of services (QoS) for target positioning in sensing fields. However, it is possible that all regions in a sensing field have different requirements for target positioning accuracy. We also concern the terrain of sensing fields might have some limitations for placing sensors. Therefore, this paper proposes a generic framework for the sensor deployment problem supporting differential quality of services (QoS) for target positioning to all regions in a sensing field. We define weighted error distance as metric of quality of positioning services. This problem is to optimize the QoS level for target positioning under the limitations of budget and discrimination priorities of regions, where locations and sensing radiuses of all sensors should be determined. We formulate the problem as a nonlinear integer programming problem where the objective function is to minimize of the maximum weighted error distance subject to the complete coverage, deployment budget, and discrimination priority constraints. A Lagrangean relaxation (LR) based heuristic is developed to solve the NP-hard problem. Experimental results reveal that the proposed framework can provide better quality of services for positioning than the previous researches, which only handles uniform QoS requirements. Moreover we evaluate the performance of proposed algorithm. As well as we adopt the previous algorithm, ID-CODE, as the benchmark to examine the proposed heuristic. The results show the proposed algorithm is very effective in terms of deployment cost.     

能耗均衡的移动传感器节点派遣算法

在混合无线传感器网络中,移动传感器节点最耗能的操作是移动,如何减少移动传感器节点的移动距离同时能让其完成任务是一个富有挑战性的研究课题。本文提出了一个移动传感器节点的派遣算法,旨在均衡各个移动传感器节点的移动负载,并且能按优先级响应事件地点,适用于任意数量的移动传感器节点和事件地点的情况。当移动传感器节点数量大于事件地点数量时,将其转化为一个带权完全二分图上的最大匹配问题。当事件地点数量大于移动传感器节点的数量时,本文提出的算法先将事件地点聚类分簇,然后派遣移动传感器节点到各个簇中分别完成访问任务。为了减少传感器节点之间的消息传输量,本文在集中式算法的基础上又提出了一个分布式算法。仿真实验结果表明本文提出的分布式算法能有效降低传感器节点之间的消息传输量,算法能够使得整个混合无线传感器网络的生存寿命延长20%左右。     

Sensor Planning for 3D Object Search

In this paper, we provide a systematic study of the task of sensor planning for object search. The search agent's knowledge of object location is encoded as a discrete probability density which is updated whenever a sensing action occurs. Each sensing action of the agent is defined by a viewpoint, a viewing direction, a field-of-view, and the application of a recognition algorithm. The formulation casts sensor planning as an optimization problem: the goal is to maximize the probability of detecting the target with minimum cost. This problem is proved to be NP-Complete, thus a heuristic strategy is favored. To port the theoretical framework to a real working system, we propose a sensor planning strategy for a robot equipped with a camera that can pan, tilt, and zoom. In order to efficiently determine the sensing actions over time, the huge space of possible actions with fixed camera position is decomposed into a finite set of actions that must be considered. The next action is then selected from among these by comparing the likelihood of detection and the cost of each action. When detection is unlikely at the current position, the robot is moved to another position for which the probability of target detection is the highest.     

Sensor Placement for Sensing Coverage and Data Precision in Wireless Sensor Networks

We present a novel paradigm of sensor placement concerning data precision and estimation. Multiple abstract sensors are used to measure a quantity of a moving target in the scenario of a wireless sensor network. These sensors can cooperate with each other to obtain a precise estimate of the quantity in a real-time manner. We consider a problem on planning a minimum-cost scheme of sensor placement with desired data precision and resource consumption. Measured data is modeled as a Gaussian random variable with a changeable variance. A gird model is used to approximate the problem. We solve the problem with a heuristic algorithm using branch-and-bound method and tabu search. Our experiments demonstrate that the algorithm is correct in a certain tolerance, and it is also efficient and scalable.     

基于能量均衡的最大化覆盖目标算法

目标覆盖问题是无线传感网络WSNs(Wireless sensor networks)最重要的问题之一.每个目标至少被一个传感节点覆盖,为此提出基于能量均衡的最大化覆盖目标EMNL(Energy-balance-based Maximizing Network Lifetime)算法.EMNL算法将所有传感节点划分不同的传感节点覆盖区SC(Sensor Cover),致使每个SC能够维持对所有目标监测一个固定时间.通过有选择性选择一个SC活动,而其他SC休眠,进而提高能量利用率,延长了网络寿命.EMNL算法构建了不同不相邻SC,进而最大化网络寿命.最后,建立仿真环境,并进行性能仿真.此环境下的数据表明,在EMNL算法有效地扩延生存时间,也提升了覆盖率.     

Lifetime maximization considering target coverage and connectivity in directional image/video sensor networks

A directional sensor network consists of a large number of directional sensors (e.g., image/video sensors), which have a limited angle of sensing range due to technical constraints or cost considerations. In such directional sensor networks, the power saving issue is a challenging problem. In this paper, we address the Directional Cover and Transmission (DCT) problem of organizing the directional sensors into a group of non-disjoint subsets to extend the network lifetime. One subset in which the directional sensors cover all the targets and forward the sensed data to the sink is activated at one time, while the others sleep to conserve their energy. For the DCT problem proven to be the NP-complete problem, we present a heuristic algorithm called the Shortest Path from Target to Sink (SPTS)-greedy algorithm. To verify and evaluate the proposed algorithm, we conduct extensive simulations and show that it can contribute to extending the network lifetime to a reasonable extent.     

Minimal sensor integrity: Measuring the vulnerability of sensor grids

Given the increasing importance of optimal sensor deployment for battlefield strategists, the converse problem of reacting to a particular deployment by an enemy is equally significant and not yet addressed in a quantifiable manner in the literature. We address this issue by modeling a two stage game in which the opponent deploys sensors to cover a sensor field and we attempt to maximally reduce his coverage at minimal cost. In this context, we introduce the concept of minimal sensor integrity which measures the vulnerability of any sensor deployment. We find the best response by quantifying the merits of each response. While the problem of optimally deploying sensors subject to coverage constraints is NP-complete [Chakrabarty et al., IEEE Trans. Comput., to appear], in this paper we show that the best response (i.e., the maximum vulnerability) can be computed in polynomial time for sensors with arbitrary coverage capabilities deployed over points in any dimensional space. In the special case when sensor coverages form an interval graph (as in a linear grid), we describe a better O(min(M²,NM)) dynamic programming algorithm.     

CCS-MAC: Exploiting the overheard data for compression in wireless sensor networks

Both the overhearing and overhearing avoidance in a densely distributed sensor network may inevitably incur considerable power consumption. In this paper we propose a so-called CCS-MAC (collaborative compression strategy-based MAC) MAC protocol which facilitates to exploit those overheard data that is treated useless in traditional MAC protocols for the purpose of cost and energy savings. Particularly the CCS-MAC enables different sensor nodes to perform data compression cooperatively with regard to those overheard data, so that the redundancy of data prepared for the link layer transmission can be totally eliminated at the earliest. The problem of collaborative compression is analyzed and discussed along with a corresponding linear programming model formulated. Based on it a heuristic node-selection algorithm with a time complexity of (O(N²)) is proposed to the solve the linear programming problem. The node-selection algorithm is implemented in CCS-MAC at each sensor node in a distributed manner. The experiment results verify that the proposed CCS-MAC scheme can achieve a significant energy savings so as to prolong the lifetime of the sensor networks so far.     

Star p-hub median problem with modular arc capacities

We consider the hub location problem, where p hubs are chosen from a given set of nodes, each nonhub node is connected to exactly one hub and each hub is connected to a central hub. Links are installed on the arcs of the resulting network to route the traffic. The aim is to find the hub locations and the connections to minimize the link installation cost. We propose two formulations and a heuristic algorithm to solve this problem. The heuristic is based on Lagrangian relaxation and local search. We present computational results where formulations are compared and the quality of the heuristic solutions are tested.     

Distributed algorithms for barrier coverage using relocatable sensors

We study the barrier coverage problem using relocatable sensor nodes. We assume each sensor can sense an intruder or event inside its sensing range. Sensors are initially located at arbitrary positions on the barrier and can move along the barrier. The goal is to find final positions for sensors so that the entire barrier is covered. In recent years, the problem has been studied extensively in the centralized setting. In this paper, we study a barrier coverage problem in the distributed and discrete setting. We assume that we have n identical sensors located at grid positions on the barrier, and that each sensor repeatedly executes a Look-Compute-Move cycle: based on what it sees in its vicinity, it makes a decision on where to move, and moves to its next position. We make two strong but realistic restrictions on the capabilities of sensors: they have a constant visibility range and can move only a constant distance in every cycle. In this model, we give the first two distributed algorithms that achieve barrier coverage for a line segment barrier when there are enough nodes in the network to cover the entire barrier. Our algorithms are synchronous, and local in the sense that sensors make their decisions independently based only on what they see within their constant visibility range. One of our algorithms is oblivious whereas the other uses two bits of memory at each sensor to store the type of move made in the previous step. We show that our oblivious algorithm terminates within \(\varTheta (n^2)\) steps with the barrier fully covered, while the constant-memory algorithm is shown to take \(\varTheta (n)\) steps to terminate in the worst case. Since any algorithm in which a sensor can only move a constant distance in one step requires \(\varOmega (n)\) steps on some inputs, our second algorithm is asymptotically optimal.     

On the problem of k-coverage in mission-oriented mobile wireless sensor networks

The problem of sensor deployment to achieve k-coverage of a field, where every point is covered by at least k sensors, is very critical in the design of energy-efficient wireless sensor networks (WSNs). It becomes more challenging in mission-oriented WSNs, where sensors have to move in order to k-cover a region of interest in the field. In this type of network, there are multiple missions (or monitoring tasks) to be accomplished, each of which has different requirements, particularly, in terms of coverage. In this paper, we consider the problem of k-coverage in mission-oriented mobile WSNs which we divide into two sub-problems, namely sensor placement and sensor selection. The sensor placement problem is to identify a subset of sensors and their locations in a region of interest so it is k-covered with a small number of sensors. The sensor selection problem is to determine which sensors should move to the above-computed locations in the region while minimizing the total energy consumption due to sensor mobility and communication. Specifically, we propose centralized and distributed approaches to solve the k-coverage problem in mission-oriented mobile WSNs. Our solution to the sensor placement problem is based on Helly’s Theorem and the geometric analysis of the Reuleaux triangle. First, we consider a deterministic (or disk) sensing model, where the sensing range is modeled as a disk. Then, based on the above analysis, we address the k-coverage problem using a more realistic sensing model, known as probabilistic sensing model. The latter reflects the stochastic nature of the characteristics of the sensors, namely sensing and communication ranges. Our centralized and distributed protocols enable the sensors to move toward a region of interest and k-cover it with a small number of sensors. Our experiments show a good match between simulation and analytical results. In particular, simulation results show that our solution to the k-coverage problem in mission-oriented mobile WSNs outperforms an existing one in terms of the number of sensors needed to k-cover a region of interest in the field and their total energy consumption due to communication, sensing, and mobility for the correct operation of the protocol.     

A two-phase hybrid heuristic algorithm for the capacitated location-routing problem

In this paper, we propose a two-phase hybrid heuristic algorithm to solve the capacitated location-routing problem (CLRP). The CLRP combines depot location and routing decisions. We are given on input a set of identical vehicles (each having a capacity and a fixed cost), a set of depots with restricted capacities and opening costs, and a set of customers with deterministic demands. The problem consists of determining the depots to be opened, the customers and the vehicles to be assigned to each open depot, and the routes to be performed to fulfill the demand of the customers. The objective is to minimize the sum of the costs of the open depots, of the fixed cost associated with the used vehicles, and of the variable traveling costs related to the performed routes. In the proposed hybrid heuristic algorithm, after a Construction phase (first phase), a modified granular tabu search, with different diversification strategies, is applied during the Improvement phase (second phase). In addition, a random perturbation procedure is considered to avoid that the algorithm remains in a local optimum for a given number of iterations. Computational experiments on benchmark instances from the literature show that the proposed algorithm is able to produce, within short computing time, several solutions obtained by the previously published methods and new best known solutions.     

Biologically inspired probabilistic coverage for mobile sensor networks

Coupling sensors in a sensor network with mobility mechanism can boost the performance of wireless sensor networks (WSNs). In this paper, we address the problem of self-deploying mobile sensors to reach high coverage. The problem is modeled as a multi-objective optimization that simultaneously minimizes two contradictory parameters; the total sensor moving distance and the total uncovered area. In order to resolve the aforementioned deployment problem, this study investigates the use of biologically inspired mechanisms, including evolutionary algorithms and swarm intelligence, with their state-of-the-art algorithms. Unlike most of the existing works, the coverage parameter is expressed as a probabilistic inference model due to uncertainty in sensor readings. To the best of our knowledge, probabilistic coverage of mobile sensor networks has not been addressed in the context of multi-objective bio-inspired algorithms. Performance evaluations on deployment quality and deployment cost are measured and analyzed through extensive simulations, showing the effectiveness of each algorithm under the developed objective functions. Simulations reveal that only one multi-objective evolutionary algorithm; the so-called multi-objective evolutionary algorithm with decomposition survives to effectively tackle the probabilistic coverage deployment problem. It gathers more than 78 % signals from all of the targets (and in some cases reaches 100 % certainty). On the other hand, non-dominated sorting genetic algorithm II, multi-objective particle swarm optimization, and non-dominated sorting particle swarm optimization show inferior performance down to 16–32 %, necessitating further modifications in their internal mechanisms.     

Using mobile beacons to locate sensors in obstructed environments

Locating sensors in an indoor environment is a challenging problem due to the insufficient distance measurements caused by short ultrasound range and the incorrect distance measurements caused by multipath effect of ultrasound. In this paper, we propose a virtual ruler approach, in which a vehicle equipped with multiple ultrasound beacons travels around the area to measure distances between pairwise sensors. Virtual Ruler can not only obtain sufficient distances between pairwise sensors, but can also eliminate incorrect distances in the distance measurement phase of sensor localization. We propose to measure the distance between pairwise sensors from multiple perspectives using the virtual ruler and filter incorrect values through a statistical approach. By assigning measured distances with confidence values, the localization algorithm can intelligently localize each sensor based on high confidence distances, which greatly improves localization accuracy. Our performance evaluation shows that the proposed approach can achieve better localization results than previous approaches in an indoor environment.     

一种混合无线传感器网络内覆盖洞修补的分布式启发算法

首先对最小化最大移动开销移动传感器分布式算法设计进行了分析, 并指出在分布式条件下难以对此类算法中的输出分派移动传感器的最大开销进行限制, 随后提出了一种分布式启发算法。该算法将移动传感器和覆盖洞视为节点, 在节点和节点的邻居间通过有限数量消息实现匹配。仿真结果显示, 算法可实现最高达到85%的覆盖洞修补率以及较低的移动传感器最大移动开销, 使其更能适用于实际无线传感器网络环境。     

Managing cohort movement of mobile sensors via GPS-free and compass-free node localization

A critical problem in mobile ad hoc wireless sensor networks is each node’s awareness of its position relative to the network. This problem is known as localization. In this paper, we introduce a variant of this problem, directional localization, where each node must be aware of both its position and orientation relative to its neighbors. Directional localization is relevant for applications that require uniform area coverage and coherent movement. Using global positioning systems for localization in large scale sensor networks may be impractical in enclosed spaces, and might not be cost effective. In addition, a set of pre-existing anchors with globally known positions may not always be available. In this context, we propose two distributed algorithms based on directional localization that facilitate the collaborative movement of nodes in a sensor network without the need for global positioning systems, seed nodes or a pre-existing infrastructure such as anchors with known positions. Our first algorithm, GPS-free Directed Localization (GDL) assumes the availability of a simple digital compass on each sensor node. We relax this requirement in our second algorithm termed GPS- and Compass-free Directed Localization (GCDL). Through experimentation, we demonstrate that our algorithms scale well for large numbers of nodes and provide convergent localization over time, despite errors introduced by motion actuators and distance measurements. In addition, we introduce mechanisms to preserve swarm formation during directed sensor network mobility. Our simulations confirm that, in a number of realistic scenarios, our algorithms provide for a mobile sensor network that preserves its formation over time, irrespective of speed and distance traveled. We also present our method to organize the sensor nodes in a polygonal geometric shape of our choice even in noisy environments, and investigate the possible uses of this approach in search-and-rescue type of missions.     

Optimal sensor scheduling for hidden Markov model state estimation

Consider the Hidden Markov model where the realization of a single Markov chain is observed by a number of noisy sensors. The sensor scheduling problem for the resulting hidden Markov model is as follows: design an optimal algorithm for selecting at each time instant, one of the many sensors to provide the next measurement. Each measurement has an associated measurement cost. The problem is to select an optimal measurement scheduling policy, so as to minimize a cost function of estimation errors and measurement costs. The problem of determining the optimal measurement policy is solved via stochastic dynamic programming. Numerical results are presented.     

Sensor deployment optimization methods to achieve both coverage and connectivity in wireless sensor networks

In this study, we aim to cover a sensing area by deploying a minimum number of wireless sensors while maintaining the connectivity between the deployed sensors. The problem may be reduced to a two-dimensional critical coverage problem which is an NP-Complete problem. We develop an integer linear programming model to solve the problem optimally. We also propose a local search (LS) algorithm and a genetic algorithm (GA) as approximate methods. We verify by computational experiments that the integer linear model, using Cplex, is able to provide an optimal solution of all our small and medium size problems. We also show that the proposed methods outperform some regular sensor deployment patterns.     

A routing algorithm based on clusters and balanced use of energy for wireless sensor networks

In a sensor network,data collected by different sensors are often correlated because they are observations of related phenomena.Efficient sensor data fusion is one of the most important issues in building real sensor networks.To balance energy cost,how to select a cluster head is a key problem that must be addressed.In this paper,we use a compression-centric data collection algorithm for use in wireless sensor networks.Also,we propose a balanced cluster head selection algorithm in each cluster.Simulation re...