Sensor Deployment Problem Research Articles

A Novel Minimization Method for Sensor Deployment Via Heuristic 2-Sat Solution

The tasks of guard placement or sensor deployment in an art gallery, a museum or in the corridors of public and security buildings pose the same problem, which requires placing the guards or sensors so as to cover a specified set of nodes with a minimum number of sensors or guards, thereby reducing the overall cost of the system as well assist power consumption. Generally, minimization can be done using optimization techniques such as linear programming, but in case of sensor deployment or guard placement there is a need either to place or not to place the sensor or guard, and hence only Boolean or binary values are used. Therefore, in order to optimize such a problem, we use the special case of linear integer programming known as Boolean integer linear programming (0-1 ILP). Other algorithms like Pseudo-Boolean SAT Solvers can also be used for the minimization purpose. In this paper, we introduce these minimization algorithms for the sensor deployment problem. We also contribute a greed-based heuristic, which utilizes the fact that the pertinent propositional formulas have variables of purely un-complemented literals. This heuristic has much less computational cost compared to those of 0-1 ILP and the Pseudo-Boolean SAT Solvers.

Movement-Efficient Sensor Deployment in Wireless Sensor Networks With Limited Communication Range

We study a mobile wireless sensor network (MWSN) consisting of multiple mobile sensors or robots. Three key factors in MWSNs, sensing quality, energy consumption, and connectivity, have attracted plenty of attention, but the interaction of these factors is not well studied. To take all the three factors into consideration, we model the sensor deployment problem as a constrained source coding problem. %, which can be applied to different coverage tasks, such as area coverage, target coverage, and barrier coverage. Our goal is to find an optimal sensor deployment (or relocation) to optimize the sensing quality with a limited communication range and a specific network lifetime constraint. We derive necessary conditions for the optimal sensor deployment in both homogeneous and heterogeneous MWSNs. According to our derivation, some sensors are idle in the optimal deployment of heterogeneous MWSNs. Using these necessary conditions, we design both centralized and distributed algorithms to provide a flexible and explicit trade-off between sensing uncertainty and network lifetime. The proposed algorithms are successfully extended to more applications, such as area coverage and target coverage, via properly selected density functions. Simulation results show that our algorithms outperform the existing relocation algorithms.

Deployment of Context-Aware Sensor in Wireless Sensor Network Based on the Variants of Genetic Algorithm

Wireless sensor networks (WSNs) are generally a group of spatially scattered and devoted sensors to record and monitor the physical environmental condition, and the collected data is grouped at a central location. In fact, the environmental conditions such as sound, humidity, temperature, wind, pollution levels, etc., can be clearly determined by WSNs. The principal objective of WSNs is to organize the whole sensor nodes in their related positions, thereby developing an effective network. In WSNs, target COVerage (TCOV) and Network CONnectivity (NCON) are the main concern of the sensor deployment problem. Many research works aspire the evolvement of smart context awareness algorithm for sensor deployment issues in WSN. Here the TCOV and NCON process are deployed as the minimization problem. This article makes an analysis of different GA variations in attaining the objective. The GA variations are as follows: self-adaptive genetic algorithm (SAGA), deterministic-adaptive genetic algorithm (DAGA), Individual- Adaptive Genetic Algorithm (IAGA). Finally, the methods are compared to one another in terms of connectivity and coverage performance.

Minimizing movement for network connectivity in mobile sensor networks: an adaptive approach

Nowadays the most important key point in the design of wireless sensor networks (WSNs) is the sensor coverage point and network connectivity. The main practical issue in designing the essential WSN is the mobility of mobile sensors which consumes more power thereby reduces the network lifetime significantly. In order to avoid these problems, we have investigated the mobile sensor deployment (MSD) problem comprises of network connectivity and target coverage is resolved using Euclidean spanning tree model (ECST) and ECST-adaptive VABC (ECST-AVABC) method respectively. Besides, we proposed an AVABC optimization algorithm by obtaining minimum movement of mobile sensors over the network. Furthermore, the extensive simulation experiments have offered the optimal promising solutions of NCON, to the MSD problem with minimum movement and providing the extended lifetime of WSN. Finally, the experimental results states that the movement distance shown by the proposed ECST-AVABC become 4.2, 10, and 20% lesser than the standard ECST-VABC method.

Optimal Node Deployment and Energy Provision for Wirelessly Powered Sensor Networks

In a typical wirelessly powered sensor network (WPSN), wireless chargers provide energy to sensor nodes by using wireless energy transfer (WET). The chargers can greatly improve the lifetime of a WPSN using energy beamforming by a proper charging scheduling of energy beams. However, the supplied energy still may not meet the demand of the energy of the sensor nodes. This issue can be alleviated by deploying redundant sensor nodes, which not only increase the total harvested energy but also decrease the energy consumption per node provided that an efficient scheduling of the sleep/awake of the nodes is performed. Such a problem of joint optimal sensor deployment, WET scheduling, and node activation is posed and investigated in this paper. The problem is an integer optimization that is challenging due to the binary decision variables and non-linear constraints. Based on the analysis of the necessary condition such that the WPSN be immortal, we decouple the original problem into a node deployment problem and a charging and activation scheduling problem. Then, we propose an algorithm and prove that it achieves the optimal solution under a mild condition. The simulation results show that the proposed algorithm reduces the needed nodes to deploy by approximately 16%, compared with a random-based approach. The simulation also shows that if the battery buffers are large enough, the optimality condition will be easy to meet.

Novel Visual Sensor Deployment Algorithm in Occluded Wireless Visual Sensor Networks

In this paper, the visual sensor coverage and deployment problem in occluded wireless visual sensor network (WVSN) is studied. A new occlusion probability model is proposed to compute the occlusion probability from the fixed obstacles and the moving obstacles. Based on this occlusion probability model, an optimization formulation is developed, and then, a novel heuristic algorithm (FoVIC) is devised. The basic idea of the FoVIC algorithm is to deploy a sensor, one at a time, that can cover the largest number of uncovered objects with highest nonoccluded probability. In identifying a new sensor to be deployed, the FoVIC algorithm removes the removable sensors deployed at the earlier stages. The algorithm repeats until all the objects of interest are covered. From the computational experiments, it shows that, in occluded WVSN, the larger span angle can help to reduce the number of deployed sensors than the sensing range. Moreover, the FoVIC algorithm performs well in both random occlusion probability scenario and obstacle-location-dependent occlusion probability scenario. When considering the sensor deployment cost, the FoVIC intelligently expands the sensing range of existing sensors in high-occluded WVSN and deploys a new sensor in low-occluded WVSN to minimize the total cost.

On Efficient Deployment of Wireless Sensors for Coverage and Connectivity in Constrained 3D Space.

Sensor networks have been used in a rapidly increasing number of applications in many fields. This work generalizes a sensor deployment problem to place a minimum set of wireless sensors at candidate locations in constrained 3D space to k-cover a given set of target objects. By exhausting the combinations of discreteness/continuousness constraints on either sensor locations or target objects, we formulate four classes of sensor deployment problems in 3D space: deploy sensors at Discrete/Continuous Locations (D/CL) to cover Discrete/Continuous Targets (D/CT). We begin with the design of an approximate algorithm for DLDT and then reduce DLCT, CLDT, and CLCT to DLDT by discretizing continuous sensor locations or target objects into a set of divisions without sacrificing sensing precision. Furthermore, we consider a connected version of each problem where the deployed sensors must form a connected network, and design an approximation algorithm to minimize the number of deployed sensors with connectivity guarantee. For performance comparison, we design and implement an optimal solution and a genetic algorithm (GA)-based approach. Extensive simulation results show that the proposed deployment algorithms consistently outperform the GA-based heuristic and achieve a close-to-optimal performance in small-scale problem instances and a significantly superior overall performance than the theoretical upper bound.

A Hybrid Algorithm for Optimal Wireless Sensor Network Deployment with the Minimum Number of Sensor Nodes

Wireless sensor network (WSN) applications are rapidly growing and are widely used in various disciplines. Deployment is one of the key issues to be solved in WSNs, since the sensor nodes’ positioning affects highly the system performance. An optimal WSN deployment should maximize the collection of the desired interest phenomena, guarantee the required coverage and connectivity, extend the network lifetime, and minimize the network cost in terms of energy consumption. Most of the research effort in this area aims to solve the deployment issue, without minimizing the network cost by reducing unnecessary working nodes in the network. In this paper, we propose a deployment approach based on the gradient method and the Simulated Annealing algorithm to solve the sensor deployment problem with the minimum number of sensor nodes. The proposed algorithm is able to heuristically optimize the number of sensors and their positions in order to achieve the desired application requirements.

A Fault Tolerance Mechanism for On-Road Sensor Networks.

On-Road Sensor Networks (ORSNs) play an important role in capturing traffic flow data for predicting short-term traffic patterns, driving assistance and self-driving vehicles. However, this kind of network is prone to large-scale communication failure if a few sensors physically fail. In this paper, to ensure that the network works normally, an effective fault-tolerance mechanism for ORSNs which mainly consists of backup on-road sensor deployment, redundant cluster head deployment and an adaptive failure detection and recovery method is proposed. Firstly, based on the N − x principle and the sensors’ failure rate, this paper formulates the backup sensor deployment problem in the form of a two-objective optimization, which explains the trade-off between the cost and fault resumption. In consideration of improving the network resilience further, this paper introduces a redundant cluster head deployment model according to the coverage constraint. Then a common solving method combining integer-continuing and sequential quadratic programming is explored to determine the optimal location of these two deployment problems. Moreover, an Adaptive Detection and Resume (ADR) protocol is deigned to recover the system communication through route and cluster adjustment if there is a backup on-road sensor mismatch. The final experiments show that our proposed mechanism can achieve an average 90% recovery rate and reduce the average number of failed sensors at most by 35.7%.

On Cost-Efficient Sensor Placement for Contaminant Detection in Water Distribution Systems

In recent years, water pollution or contamination incidents happened frequently, causing serious disasters and negative social impact. To reduce the water contamination risk, water quality monitoring sensors should be deployed in water distribution system (WDS) to enable real-time pollution detection. It is desirable to deploy sensors everywhere so that any contamination event can be detected and reported in a timely manner. Unfortunately, this is a luxury and unrealistic vision because of high deployment cost. It is significant to lower the deployment cost provided that the quality-of-sensing, e.g., coverage and contamination detection time, can be guaranteed for effective depollution action. In this paper, we consider a water quality monitoring sensor network consisting of two kinds of sensors with different prices. The expensive one is of cellular communication capability and therefore is able to send sensing information to control center directly, while the cheaper one is of only sensor-to-sensor communication capability. We investigate a cost-efficient sensor deployment problem on how to deploy these two kinds of sensors in a given WDS to minimize the deployment cost, without violating the quality-of-sensing requirement. We first formulate the problem into a mixed integer quadratically constrained programming problem, which is then linearized into an equivalent mixed integer linear programming. We further propose a polynomial two-stage heuristic algorithm and evaluate its efficiency via extensive simulation-based studies.

Sensor Deployment With Limited Communication Range in Homogeneous and Heterogeneous Wireless Sensor Networks

We study the heterogeneous wireless sensor networks (WSNs) and propose the necessary condition of the optimal sensor deployment. Similar to that in homogeneous WSNs, the necessary condition implies that every sensor node location should coincide with the centroid of its own optimal sensing region. Moreover, we discuss the dynamic sensor deployment in both the homogeneous and the heterogeneous WSNs with limited communication range for the sensor nodes. The purpose of sensor deployment is to improve sensing performance, reflected by distortion and coverage. We model the sensor deployment problem as a source coding problem with distortion reflecting sensing accuracy. However, when the communication range is limited, a WSN is divided into several disconnected sub-graphs under certain conditions as we will discuss in this paper. In such a scenario, neither the conventional distortion nor the coverage represents the sensing performance as the collected data in disconnected sub-graphs cannot be communicated with the access point. By defining an appropriate sensing performance measure, we propose a Restrained Lloyd (RL) algorithm and a deterministic annealing (DA) algorithm to optimize sensor deployment in both the homogeneous and heterogeneous WSNs. Our simulation results show that both the DA and the RL algorithms outperform the existing algorithms when communication range is limited.

A multi-objective optimization approach to optimal sensor location problem in IGCC power plants

Integrated Gasification Combined Cycle (IGCC) power plants provide a cleaner and more efficient way to obtain energy from coal. In order to operate an IGCC power plant in a safe and stable manner, several input and output process parameters need to be monitored. However, due to economic and operational constraints, it is infeasible to place sensors at every input and output process parameter location. Hence, it becomes important to select the most effective sensor locations which lead to maximum information gain about the plant conditions. Practical issues present in an IGCC power plant, such as harsh physical conditions and variability in process parameters, make the optimal sensor location problem an especially complicated one. Further, sensors can have multiple objectives and they can produce uncertainties due to measurement errors. This work considers hybrid hardware and virtual sensing for advanced power systems with multiple objectives. In order to solve this real world large scale problem, we use a novel algorithm called Better Optimization of Nonlinear Uncertain Systems (BONUS). BONUS works in probability distribution space and avoids sampling for each optimization and derivative calculations iterations. A new algorithm for multi-objective optimization is also developed specifically for problem. The result of this nonlinear stochastic multi-objective problem is the non-dominated, or Pareto set, which provides trade-offs between various objectives like observability, cost, and thermal efficiency. This is the first attempt at solving the problem of optimal sensor deployment in advanced power plants, with consideration of hybrid hardware and virtual sensing and incorporation of uncertainty with multiple objectives.

Autonomous deployment of wireless sensor networks for optimal coverage with directional sensing model

Sensor deployment is an important aspect of network architecture for Wireless Sensor Networks (WSNs). Although many solutions to mobile sensors deployment have been proposed, controlling mobile sensors with directional sensing ability towards optimal coverage remains to be an open problem. In this paper, we take the initiative to handle the Coverage Maximizing Mobile Sensor Deployment Problem (CMMSDP) with directional and arbitrarily oriented sensors. Our proposal consists of two algorithms. The first one, Concurrent Rotation and Motion Control (CRMC), is a localized iterative algorithm derived from optimality conditions, so it aims at reaching local maximum. The second algorithm, Staged Rotation and Motion Control (SRMC), decouples rotation and motion controls in order to reduce the computation complexity with slight sacrifice in optimality. We derive optimality and complexity results for both algorithms. We also implement our algorithms in TOSSIM and evaluate them against commonly used metrics. The promising results confirm the absolute feasibility of our proposals.

A Novel Sensor Deployment Method Based on Image Processing and Wavelet Transform to Optimize the Surface Coverage in WSNs

A novel sensor deployment method utilize Discrete wavelet transform (DWT) is proposed, and the DWT is used to calculate the sub-band energy entropy to ascertain the coverage cavities in Wireless sensor networks (WSNs). We address the problem of deploying a limit number of sensors to optimize the coverage ratio in 3D surface, while it is a complex surface in space and sensors can be deployed only onto it. Another novel aspect of this paper is that the method followed utilizes an Artificial bee colony algorithm with dynamic search strategy (ABC-DSS), which mimics the behavior of bees, and the new modified ABC-DSS algorithm matches the sensor deployment problems on 3D surface well. The extensive simulations illustrate that comparing with the deployment method based on Particle swarm optimization (PSO) and ABC, the ABC-DSS which utilizes the wavelet sub-band energy entropy is functional and efficient on 3D surface deployment problems.

An Efficient Large-Scale Sensor Deployment Using a Parallel Genetic Algorithm Based on CUDA

We have employed evolutionary computation to solve the optimization problem of sensor deployment in battlefield environments. A genetic algorithm has the advantage of delivering results of a higher quality than simple computational algorithms, but it has the drawback of requiring too much computing time. This study aimed not only to shorten the computing time to as close to real-time as possible by using the Compute Unified Device Architecture (CUDA) but also to maintain a solution quality that is as good as or better than the case when the proposed algorithm is not used. In the proposed genetic algorithm, parallelization was applied to speed up the fitness evaluation requiring heavy computation time. The proposed CUDA-based design approach for complex and various sensor deployments is validated by means of simulation. We parallelized two parts in Monte Carlo simulation for the fitness evaluation: moving lots of tested vehicles and calculating the probability of detection (POD) for each vehicle. The experiment was divided into CPU and GPU experiments depending on arithmetic unit types. In the GPU experiment, the results showed similar levels for the detection probability by GPU and CPU, and the computing time decreased by approximately 55-56 times.

Lifetime optimisation for linear wireless sensor networks under retransmission

The lifetime of wireless sensor networks (WSN) is strongly affected by the energy consumption, which is partly attributed to retransmission. In this paper, the energy consumption of a linear WSN is analysed based on basic energy consumption models and a retransmission model. Optimisation models are proposed with the objective of maximising WSN lifetime under the coverage and success transmission rate constraints. In particular, for traditional uniform and non-uniform sensor deployment problems without considering the sleep/wakeup mechanism, the number of sensors and their distances are optimised. Moreover, for a linear WSN with a sleep/wakeup mechanism, a sensor deployment scheme is developed by optimally assigning sensors to fixed locations. The genetic algorithm and the generalised reduced gradient method are applied to solve these problems. Our case studies illustrate that the lifetime of WSN with and without retransmission are different, and it is important to consider retransmission in WSN lifetime optimisation.

No Correlations Involved: Decision Making Under Uncertainty in a Conservative Sparse Information Space

This letter is concerned with decision making under uncertainty in problems involving high dimensional state spaces. Inspired by conservative information fusion techniques, we propose a novel paradigm where decision making is performed over a conservative rather than the original information space. The key idea is that regardless of the sparsity pattern of the latter, one can always calculate a sparse conservative information space, which admits computationally efficient decision making. In this letter, we take this concept to the extreme and consider a conservative approximation that decouples the state variables, leading to a conservative diagonal information matrix. As a result, the computational complexity involved with evaluating impact of a candidate action is reduced to $O(n)$ , for an $n$ -dimensional state, as the calculations do not involve any correlations. Importantly, we show that for measurement observation models involving arbitrary single state variables, this concept yields exactly the same results compared to using the original information matrix. We demonstrate applicability of this concept to a sensor deployment problem.

Exact methods for sensor deployment problem with connectivity constraint in wireless sensor networks

In wireless sensor networks, coverage and connectivity are two essential issues. They indicate how all the points of an area of interest are covered and how the sensor devices of the wireless sensor network are connected in an efficient way. In this study, the problem of minimising total grid coverage cost with connectivity constraint is considered. The connectivity constraint means that each deployed sensor has to find a path, composed of connected sensors, until to reach the base station the sink. The problem may be reduced to a 2-dimensional critical grid coverage problem which is an NP-Complete problem. We propose mixed integer linear programming models to solve the problem optimally. We verify by computational experiments that the developed approaches can provide the optimal solution of grid sizes of 15 × 15 width × length in a reasonable time. We also show that one of the proposed methods is more efficient than some methods developed for similar problems.

Lifetime optimisation for linear wireless sensor networks under retransmission

The lifetime of wireless sensor networks WSN is strongly affected by the energy consumption, which is partly attributed to retransmission. In this paper, the energy consumption of a linear WSN is analysed based on basic energy consumption models and a retransmission model. Optimisation models are proposed with the objective of maximising WSN lifetime under the coverage and success transmission rate constraints. In particular, for traditional uniform and non-uniform sensor deployment problems without considering the sleep/wakeup mechanism, the number of sensors and their distances are optimised. Moreover, for a linear WSN with a sleep/wakeup mechanism, a sensor deployment scheme is developed by optimally assigning sensors to fixed locations. The genetic algorithm and the generalised reduced gradient method are applied to solve these problems. Our case studies illustrate that the lifetime of WSN with and without retransmission are different, and it is important to consider retransmission in WSN lifetime optimisation.

Sensor Network Deployment under Distance Uncertainty with Robust Optimization

We consider the sensor deployment problem in the context of distance uncertainty. It is characterized by differentiated arrangement of specific detection probability thresholds at different locations. The problem is formulated as an integer linear programming (ILP) model firstly, aiming at optimizing the number of sensors and their locations. Based on the robust discrete optimization methodology, the uncertain model is transformed into an equivalent ILP problem considering distance uncertainty. The proposed approach can control the tradeoff between optimality and robustness by varying the parameters named protection levels. Uniform and non-uniform event detection probabiliy distributions are considered in the experiment. The results show that, as the distance uncertainty increases, the constraint violation can be avoided in the robust model and the robust solution can provide a significant improvement at the expense of a small loss in optimality when compared to the optimal solution of a deterministic scenario.