Random vs. Deterministic Deployment of Sensors in the Presence of Failures and Placement Errors

Abstract—Although random deployment is widely used in theoretical analysis of coverage and connectivity, and evaluation of various algorithms (e.g., sleep-wakeup), it has often been considered too expensive as compared to optimal deterministic deployment patterns when deploying sensors in real-life. Roughly speaking, a factor of log n additional sensors are needed in random deployment as compared to optimal deterministic de-ployment if n sensors are needed in a random deployment. This may be an illusion however, since all real-life large-scale deployments strategies result in some randomness, two prime sources being placement errors and sensor failures, either at the time of deployment or afterwards. In this paper, we consider the effects of placement errors and random failures on the density needed to achieve full coverage when sensors are deployed randomly versus deterministically. We compare three popular strategies for deployment. In the first strategy, sensors are deployed in an optimal lattice but enough sensors are colocated at each lattice point to compensate for failure and placement errors. In the second, only one sensor is deployed at each lattice point but lattice spacing is sufficiently shrunk to achieve a desired quality of coverage in the presence of failure and placement errors. In the third, a random deployment is used with appropriate density. We derive explicit expressions for the density needed for each of the three strategies to achieve a given quality of coverage, which are of independent interest. In comparing the three deployments, we find that if errors in placement are half the sensing range and failure probability is 50%, random deployment needs only around 10 % higher density to provide a similar quality of coverage as the other two. We provide a comprehensive comparison to help a practitioner decide the lowest cost deployment strategy in real-life. I

Distributed Emitter Detector Design under Imperfect Communication Channel

We consider the distributed detection of an emitter using multiple sensors deployed at deterministic locations. The signal from the emitter follows a signal attenuation model dependent on the distance between the sensor and the emitter. The sensors transmit their decisions to the fusion center through a parallel access Binary Symmetric Channel (BSC) with a cross-over probability. We seek to optimize the detection performance under a prescribed false alarm at the sensor level and at the system level. We consider the triangular topology structure and using the least favorable emitter range study the impact of the BSC on the system level detection fusion rules. The MAJORITY fusion rule is found to be optimal under certain conditions

Coverage-Guaranteed Sensor Node Deployment Strategies for Wireless Sensor Networks

Deployment quality and cost are two conflicting aspects in wireless sensor networks. Random deployment, where the monitored field is covered by randomly and uniformly deployed sensor nodes, is an appropriate approach for large-scale network applications. However, their successful applications depend considerably on the deployment quality that uses the minimum number of sensors to achieve a desired coverage. Currently, the number of sensors required to meet the desired coverage is based on asymptotic analysis, which cannot meet deployment quality due to coverage overestimation in real applications. In this paper, we first investigate the coverage overestimation and address the challenge of designing coverage-guaranteed deployment strategies. To overcome this problem, we propose two deployment strategies, namely, the Expected-area Coverage Deployment (ECD) and BOundary Assistant Deployment (BOAD). The deployment quality of the two strategies is analyzed mathematically. Under the analysis, a lower bound on the number of deployed sensor nodes is given to satisfy the desired deployment quality. We justify the correctness of our analysis through rigorous proof, and validate the effectiveness of the two strategies through extensive simulation experiments. The simulation results show that both strategies alleviate the coverage overestimation significantly. In addition, we also evaluate two proposed strategies in the context of target detection application. The comparison results demonstrate that if the target appears at the boundary of monitored region in a given random deployment, the average intrusion distance of BOAD is considerably shorter than that of ECD with the same desired deployment quality. In contrast, ECD has better performance in terms of the average intrusion distance when the invasion of intruder is from the inside of monitored region

Management And Security Of Multi-Cloud Applications

Single cloud management platform technology has reached maturity and is quite successful in information technology applications. Enterprises and application service providers are increasingly adopting a multi-cloud strategy to reduce the risk of cloud service provider lock-in and cloud blackouts and, at the same time, get the benefits like competitive pricing, the flexibility of resource provisioning and better points of presence. Another class of applications that are getting cloud service providers increasingly interested in is the carriers\u27 virtualized network services. However, virtualized carrier services require high levels of availability and performance and impose stringent requirements on cloud services. They necessitate the use of multi-cloud management and innovative techniques for placement and performance management. We consider two classes of distributed applications – the virtual network services and the next generation of healthcare – that would benefit immensely from deployment over multiple clouds. This thesis deals with the design and development of new processes and algorithms to enable these classes of applications. We have evolved a method for optimization of multi-cloud platforms that will pave the way for obtaining optimized placement for both classes of services. The approach that we have followed for placement itself is predictive cost optimized latency controlled virtual resource placement for both types of applications. To improve the availability of virtual network services, we have made innovative use of the machine and deep learning for developing a framework for fault detection and localization. Finally, to secure patient data flowing through the wide expanse of sensors, cloud hierarchy, virtualized network, and visualization domain, we have evolved hierarchical autoencoder models for data in motion between the IoT domain and the multi-cloud domain and within the multi-cloud hierarchy

Deployment Policies to Reliably Maintain and Maximize Expected Coverage in a Wireless Sensor Network

The long-term operation of a wireless sensor network (WSN) requires the deployment of new sensors over time to restore any loss in network coverage and communication ability resulting from sensor failures. Over the course of several deployment actions it is important to consider the cost of maintaining the WSN in addition to any desired performance measures such as coverage, connectivity, or reliability. The resulting problem formulation is approached first through a time-based deployment model in which the network is restored to a fixed size at periodic time intervals. The network destruction spectrum (D-spectrum) has been introduced to estimate reliability and is more commonly applied to a static network, rather than a dynamic network where new sensors are deployed over time. We discuss how the D-spectrum can be incorporated to estimate reliability of a time-based deployment policy and the features that allow a wide range of deployment policies to be evaluated in an efficient manner. We next focus on a myopic condition-based deployment model where the network is observed at periodic time intervals and a fixed budget is available to deploy new sensors with each observation. With a limited budget available the model must address the complexity present in a dynamic network size in addition to a dynamic network topology, and the dependence of network reliability on the deployment action. We discuss how the D-spectrum can be applied to the myopic condition-based deployment problem, illustrating the value of the D-spectrum in a variety of maintenance settings beyond the traditional static network reliability problem. From the insight of the time-based and myopic condition-based deployment models, we present a Markov decision process (MDP) model for the condition-based deployment problem that captures the benefit of an action beyond the current time period. Methodology related to approximate dynamic programming (ADP) and approximate value iteration algorithms is presented to search for high quality deployment policies. In addition to the time-based and myopic condition-based deployment models, the MDP model is one of the few addressing the repeated deployment of new sensors as well as an emphasis on network reliability. For each model we discuss the relevant problem formulation, methodology to estimate network reliability, and demonstrate the performance in a range of test instances, comparing to alternative policies or models as appropriate. We conclude with a stochastic optimization model focused on a slightly different objective to maximize expected coverage with uncertainty in where a sensor lands in the network. We discuss a heuristic solution method that seeks to determine an optimal deployment of sensors, present results for a wide range of network sizes and explore the impact of sensor failures on both the model formulation and resulting deployment policy

PERFORMANCE & SIMULATION ANALYSIS OF SENSOR AREA COVERAGE

Wireless sensor networks (WSNs) have been employed in numerous military and civilian applications. Some application areas are in battlefield, surveillance, biological detection, and environmental monitoring. A major challenge to such applications is the sensor areacoverage (SAC), which refers to the techniques and mechanisms of placing sensors and their coordination in a mission space (field) to monitor the physical environment in such a way to achieve the application coverage objectives. This thesis develops a sensor area coverage package (SACPac) that simulates some selected coverage algorithms and their enhancements, and analyzes their performance parameters under various scenarios. The performance parameters considered include coverage ratio, resiliency of the field coverage against sensor failures, energy efficiency, and security of communication among sensors. The thesis also develops a prototype of communication for sending the position of an object via a mobile device to the server on which SACPac runs, so that the object trajectory can be displayed. SACPac provides the foundation for further enhancements and future research. It can also be used as an educational tool for those interested in the SAC problem