TOPOLOGICAL ASPECTS OF DESIGNING FUNCTIONALLY ROBUST WIRELESS SENSOR NETWORKS

Denial-Of-Service (DOS) attack is recognized as a biggest threat against the operation of large-scale wireless sensor networks (WSN). Especially, high-mobility radio jamming like vehicles carrying radio jamming device can cause a serious damage in performance of WSNs. Because of resource-constraint design of sensor node, it is hard to provide enough protection against high-mobility jamming attack. Therefore, large-scale WSNs are extremely vulnerable to that type of DOS attack. Recognizing the importance of the problem, we conducted a simulation study to investigate the impact of radio jamming on the performance of a large-scale WSN. Based on the simulation results, the moving speed of radio jamming source has the most conspicuous effects on the WSN performance such as packet delivery success ratio and delay. As the speed changes from 8 m/sec to 1 m/sec, the success ratio drops by up to 10%. On the other hand, the delay increases by up to 55%.

Wireless charging is a greatly promising technology to resolve the energy limitation problem suffered by wireless sensor networks. In this paper, we propose an efficient mobile energy replenishment scheme based on hybrid mode, namely MERSH, which merges the advantages of the existing online and offline modes to improve the energy supplement efficiency and meanwhile to adapt well to the energy consumption dynamicity of sensors to achieve sustainable operation of wireless sensor networks. In MERSH, nodes are allowed to proactively send charging requests to the mobile charger, called MC, when the residual energy goes down a lower level. With a special-designed low-cost method, MERSH can plan an optimized path for MC every time when it charges the sensors that have sent charging requests. Moreover, to ensure energy supplement and avoid node failure due to the high dynamic energy consumption, MERSH dynamically adjusts the charging duration of each node along the calculated charging path according to its maximum tolerate charging latency and minimum charging wait time. The theoretic analysis shows that MERSH guarantees MC to move along the optimized charging path, which reduces the whole charging cost. Through extensive simulation results, we validate the effectiveness of the proposed MERSH.

PREFACE. CONTRIBUTORS. I SENSOR NETWORK OPERATIONS OVERVIEW. 1 Overview of Mission-Oriented Sensor Networks. 1.1 Introduction. 1.2 Trends in Sensor Development. 1.3 Mission-Oriented Sensor Networks: Dynamic Systems Perspective. References. II SENSOR NETWORK DESIGN AND OPERATIONS. 2 Sensor Deployment, Self-Organization, and Localization. 2.1 Introduction. 2.2 SCARE: A Scalable Self-Configuration and Adaptive Reconfiguration Scheme for Dense Sensor Networks. 2.3 Robust Sensor Positioning in Wireless Ad Hoc Sensor Networks. 2.4 Trigonometric k Clustering (TKC) for Censored Distance Estimation. 2.5 Sensing Coverage and Breach Paths in Surveillance Wireless Sensor Networks. References. 3 Purposeful Mobility and Navigation. 3.1 Introduction. 3.2 Controlled Mobility for Efficient Data Gathering in Sensor Networks with Passively Mobile Nodes. 3.3 Purposeful Mobility in Tactical Sensor Networks. 3.4 Formation and Alignment of Distributed Sensing Agents with Double-Integrator Dynamics and Actuator Saturation. 3.5 Modeling and Enhancing the Data Capacity of Wireless Sensor Networks. References. 4 Lower Layer Issues-MAC, Scheduling, and Transmission. 4.1 Introduction. 4.2 SS-TDMA: A Self-Stabilizing Medium Access Control (MAC) for Sensor Networks. 4.3 Comprehensive Performance Study of IEEE 802.15.4. 4.4 Providing Energy Efficiency for Wireless Sensor Networks Through Link Adaptation Techniques. References. 5 Network Routing. 5.1 Introduction. 5.2 Load-Balanced Query Protocols for Wireless Sensor Networks. 5.3 Energy-Efficient and MAC-Aware Routing for Data Aggregation in Sensor Networks. 5.4 LESS: Low-Energy Security Solution for Large-scale Sensor Networks Based on Tree-Ripple-Zone Routing Scheme. References. 6 Power Management. 6.1 Introduction. 6.2 Adaptive Sensing and Reporting in Energy-Constrained Sensor Networks. 6.3 Sensor Placement and Lifetime of Wireless Sensor Networks: Theory and Performance Analysis. 6.4 Algorithms for Maximizing Lifetime of Battery-Powered Wireless Sensor Nodes. 6.5 Battery Lifetime Estimation and Optimization for Underwater Sensor Networks. References. 7 Distributed Sensing and Data Gathering. 7.1 Introduction. 7.2 Secure Differential Data Aggregation for Wireless Sensor Networks. 7.3 Energy-Conserving Data Gathering Strategy Based on Trade-off Between Coverage and Data Reporting Latency in Wireless Sensor Networks. 7.4 Quality-Driven Information Processing and Aggregation in Distributed Sensor Networks. 7.5 Progressive Approach to Distributed Multiple-Target Detection in Sensor Networks. References. 8 Network Security. 8.1 Introduction. 8.2 Energy Cost of Embedded Security for Wireless Sensor Networks. 8.3 Increasing Authentication and Communication Confidentiality in Wireless Sensor Networks. 8.4 Efficient Pairwise Authentication Protocols for Sensor and Ad Hoc Networks. 8.5 Fast and Scalable Key Establishment in Sensor Networks. 8.6 Weil Pairing-Based Round, Efficient, and Fault-Tolerant Group Key Agreement Protocol for Sensor Networks. References. III SENSOR NETWORK APPLICATIONS. 9 Pursuer-Evader Tracking in Sensor Networks. 9.1 Introduction. 9.2 The Problem. 9.3 Evader-Centric Program. 9.4 Pursuer-Centric Program. 9.5 Hybrid Pursuer-Evader Program. 9.6 Efficient Version of Hybrid Program. 9.7 Implementation and Simulation Results. 9.8 Discussion and Related Work. References. 10 Embedded Soft Sensing for Anomaly Detection in Mobile Robotic Networks. 10.1 Introduction. 10.2 Mobile Robot Simulation Setup. 10.3 Software Anomalies in Mobile Robotic Networks. 10.4 Soft Sensor. 10.5 Software Anomaly Detection Architecture. 10.6 Anomaly Detection Mechanisms. 10.7 Test Bed for Software Anomaly Detection in Mobile Robot Application. 10.8 Results and Discussion. 10.9 Conclusions and Future Work. Appendix A. Appendix B. References. 11 Multisensor Network-Based Framework for Video Surveillance: Real-Time Superresolution Imaging. 11.1 Introduction. 11.2 Basic Model of Distributed Multisensor Surveillance System. 11.3 Superresolution Imaging. 11.4 Optical Flow Computation. 11.5 Superresolution Image Reconstruction. 11.6 Experimental Results. 11.7 Conclusion. References. 12 Using Information Theory to Design Context-Sensing Wearable Systems. 12.1 Introduction. 12.2 Related Work. 12.3 Theoretical Background. 12.4 Adaptations. 12.5 Design Considerations. 12.6 Case Study. 12.7 Results. 12.8 Conclusion. Appendix. References. 13 Multiple Bit Stream Image Transmission over Wireless Sensor Networks. 13.1 Introduction. 13.2 System Description. 13.3 Experimental Results. 13.4 Summary and Discussion. References. 14 Hybrid Sensor Network Test Bed for Reinforced Target Tracking. 14.1 Introduction. 14.2 Sensor Network Operational Components. 14.3 Sensor Network Challenge Problem. 14.4 Integrated Target Surveillance Experiment. 14.5 Experimental Results and Evaluation. 14.6 Conclusion. References. 15 Noise-Adaptive Sensor Network for Vehicle Tracking in the Desert. 15.1 Introduction. 15.2 Distributed Tracking. 15.3 Algorithms. 15.4 Experimental Methods. 15.5 Results and Discussion. 15.6 Conclusion. References. ACKNOWLEDGMENTS. INDEX. ABOUT THE EDITORS.

PREFACE. CONTRIBUTORS. I SENSOR NETWORK OPERATIONS OVERVIEW. 1 Overview of Mission-Oriented Sensor Networks. 1.1 Introduction. 1.2 Trends in Sensor Development. 1.3 Mission-Oriented Sensor Networks: Dynamic Systems Perspective. References. II SENSOR NETWORK DESIGN AND OPERATIONS. 2 Sensor Deployment, Self-Organization, and Localization. 2.1 Introduction. 2.2 SCARE: A Scalable Self-Configuration and Adaptive Reconfiguration Scheme for Dense Sensor Networks. 2.3 Robust Sensor Positioning in Wireless Ad Hoc Sensor Networks. 2.4 Trigonometric k Clustering (TKC) for Censored Distance Estimation. 2.5 Sensing Coverage and Breach Paths in Surveillance Wireless Sensor Networks. References. 3 Purposeful Mobility and Navigation. 3.1 Introduction. 3.2 Controlled Mobility for Efficient Data Gathering in Sensor Networks with Passively Mobile Nodes. 3.3 Purposeful Mobility in Tactical Sensor Networks. 3.4 Formation and Alignment of Distributed Sensing Agents with Double-Integrator Dynamics and Actuator Saturation. 3.5 Modeling and Enhancing the Data Capacity of Wireless Sensor Networks. References. 4 Lower Layer Issues-MAC, Scheduling, and Transmission. 4.1 Introduction. 4.2 SS-TDMA: A Self-Stabilizing Medium Access Control (MAC) for Sensor Networks. 4.3 Comprehensive Performance Study of IEEE 802.15.4. 4.4 Providing Energy Efficiency for Wireless Sensor Networks Through Link Adaptation Techniques. References. 5 Network Routing. 5.1 Introduction. 5.2 Load-Balanced Query Protocols for Wireless Sensor Networks. 5.3 Energy-Efficient and MAC-Aware Routing for Data Aggregation in Sensor Networks. 5.4 LESS: Low-Energy Security Solution for Large-scale Sensor Networks Based on Tree-Ripple-Zone Routing Scheme. References. 6 Power Management. 6.1 Introduction. 6.2 Adaptive Sensing and Reporting in Energy-Constrained Sensor Networks. 6.3 Sensor Placement and Lifetime of Wireless Sensor Networks: Theory and Performance Analysis. 6.4 Algorithms for Maximizing Lifetime of Battery-Powered Wireless Sensor Nodes. 6.5 Battery Lifetime Estimation and Optimization for Underwater Sensor Networks. References. 7 Distributed Sensing and Data Gathering. 7.1 Introduction. 7.2 Secure Differential Data Aggregation for Wireless Sensor Networks. 7.3 Energy-Conserving Data Gathering Strategy Based on Trade-off Between Coverage and Data Reporting Latency in Wireless Sensor Networks. 7.4 Quality-Driven Information Processing and Aggregation in Distributed Sensor Networks. 7.5 Progressive Approach to Distributed Multiple-Target Detection in Sensor Networks. References. 8 Network Security. 8.1 Introduction. 8.2 Energy Cost of Embedded Security for Wireless Sensor Networks. 8.3 Increasing Authentication and Communication Confidentiality in Wireless Sensor Networks. 8.4 Efficient Pairwise Authentication Protocols for Sensor and Ad Hoc Networks. 8.5 Fast and Scalable Key Establishment in Sensor Networks. 8.6 Weil Pairing-Based Round, Efficient, and Fault-Tolerant Group Key Agreement Protocol for Sensor Networks. References. III SENSOR NETWORK APPLICATIONS. 9 Pursuer-Evader Tracking in Sensor Networks. 9.1 Introduction. 9.2 The Problem. 9.3 Evader-Centric Program. 9.4 Pursuer-Centric Program. 9.5 Hybrid Pursuer-Evader Program. 9.6 Efficient Version of Hybrid Program. 9.7 Implementation and Simulation Results. 9.8 Discussion and Related Work. References. 10 Embedded Soft Sensing for Anomaly Detection in Mobile Robotic Networks. 10.1 Introduction. 10.2 Mobile Robot Simulation Setup. 10.3 Software Anomalies in Mobile Robotic Networks. 10.4 Soft Sensor. 10.5 Software Anomaly Detection Architecture. 10.6 Anomaly Detection Mechanisms. 10.7 Test Bed for Software Anomaly Detection in Mobile Robot Application. 10.8 Results and Discussion. 10.9 Conclusions and Future Work. Appendix A. Appendix B. References. 11 Multisensor Network-Based Framework for Video Surveillance: Real-Time Superresolution Imaging. 11.1 Introduction. 11.2 Basic Model of Distributed Multisensor Surveillance System. 11.3 Superresolution Imaging. 11.4 Optical Flow Computation. 11.5 Superresolution Image Reconstruction. 11.6 Experimental Results. 11.7 Conclusion. References. 12 Using Information Theory to Design Context-Sensing Wearable Systems. 12.1 Introduction. 12.2 Related Work. 12.3 Theoretical Background. 12.4 Adaptations. 12.5 Design Considerations. 12.6 Case Study. 12.7 Results. 12.8 Conclusion. Appendix. References. 13 Multiple Bit Stream Image Transmission over Wireless Sensor Networks. 13.1 Introduction. 13.2 System Description. 13.3 Experimental Results. 13.4 Summary and Discussion. References. 14 Hybrid Sensor Network Test Bed for Reinforced Target Tracking. 14.1 Introduction. 14.2 Sensor Network Operational Components. 14.3 Sensor Network Challenge Problem. 14.4 Integrated Target Surveillance Experiment. 14.5 Experimental Results and Evaluation. 14.6 Conclusion. References. 15 Noise-Adaptive Sensor Network for Vehicle Tracking in the Desert. 15.1 Introduction. 15.2 Distributed Tracking. 15.3 Algorithms. 15.4 Experimental Methods. 15.5 Results and Discussion. 15.6 Conclusion. References. ACKNOWLEDGMENTS. INDEX. ABOUT THE EDITORS.

Wireless sensor networks (WSNs) have turned to be the backbone of most present-day information technology, which supports the service-oriented architecture in a major activity. Sensor nodes and its restricted and limited resources have been a real challenge because there’s a great engagement with sensor nodes and Internet Of things (IoT). WSN is considered to be the base stone of IoT which has been widely used recently in too many applications like smart cities, industrial internet, connected cars, connected health care systems, smart grids, smart farming and it's widely used in both military and civilian applications now, such as monitoring of ambient conditions related to the environment, precious species and critical infrastructures. Secure communication and data transfer among the nodes are strongly needed due to the use of wireless technologies that are easy to eavesdrop, in order to steal its important information. However, is hard to achieve the desired performance of both WSNs and IoT and many critical issues about sensor networks are still open. The major research areas in WSN is going on hardware, operating system of WSN, localization, synchronization, deployment, architecture, programming models, data aggregation and dissemination, database querying, architecture, middleware, quality of service and security. In This paper we discuss in detail all about Wireless Sensor Networks, its classification, types, topologies, attack models and the nodes and all related issues and complications. We also preview too many challenges about sensor nodes and the proposed solutions till now and we make a spot ongoing research activities and issues that affect security and performance of Wireless Sensor Network as well. Then we discuss what’s meant by security objectives, requirements and threat models. Finally, we make a spot on key management operations, goals, constraints, evaluation metrics, different encryption key types and dynamic key management schemes.

Wireless sensor networks (WSNs) have turned to be the backbone of most present-day information technology, which supports the service-oriented architecture in a major activity. Sensor nodes and its restricted and limited resources have been a real challenge because there’s a great engagement with sensor nodes and Internet Of things (IoT). WSN is considered to be the base stone of IoT which has been widely used recently in too many applications like smart cities, industrial internet, connected cars, connected health care systems, smart grids, smart farming and it's widely used in both military and civilian applications now, such as monitoring of ambient conditions related to the environment, precious species and critical infrastructures. Secure communication and data transfer among the nodes are strongly needed due to the use of wireless technologies that are easy to eavesdrop, in order to steal its important information. However, is hard to achieve the desired performance of both WSNs and IoT and many critical issues about sensor networks are still open. The major research areas in WSN is going on hardware, operating system of WSN, localization, synchronization, deployment, architecture, programming models, data aggregation and dissemination, database querying, architecture, middleware, quality of service and security. In This paper we discuss in detail all about Wireless Sensor Networks, its classification, types, topologies, attack models and the nodes and all related issues and complications. We also preview too many challenges about sensor nodes and the proposed solutions till now and we make a spot ongoing research activities and issues that affect security and performance of Wireless Sensor Network as well. Then we discuss what’s meant by security objectives, requirements and threat models. Finally, we make a spot on key management operations, goals, constraints, evaluation metrics, different encryption key types and dynamic key management schemes.

Wireless sensor networks usually are deployed in the complex environments and take a long time to run without human intervention. In addition, the sensor nodes have limited resources and use unstable wireless link for communication, which cause wireless sensor networks various problems in the actual operation. Therefore, the real-time monitoring tools are needed to maintain the operation of wireless sensor networks, which are capable of monitoring network operating conditions, assessing network performance, detecting network failure and optimizing network operation. A network monitoring and packet sniffing tool for wireless sensor networks (NSSN) is presented and implemented in this paper. As a kind of real-time monitoring tools based sniffers, NSSN can capture the radio packets from the normal nodes using NSSNer nodes, so that it can monitor network status, find network problems and optimize network configuration without any interference in the normal operation of wireless sensor networks. The functions which NSSN has implemented include network monitoring, protocol parsing and display, network diagnosis and performance measurement, data mining and statistical analysis. According to the actual deployment, NSSN has been verified good monitoring performance.

The actual problem of developing methods of interaction in wireless sensor networks focused on energy saving is discussed. It is shown that the operation of a wireless sensor network is built taking into account compromise mechanisms that make it possible to extend the life of the network in the presence of low-power sensor nodes on which the network is built. It is concluded that it is necessary to introduce new algorithms into the operation of wireless sensor networks, which make it possible to reduce the number of operations when calculating a route, transmitting data, or other operations without losing functionality, but contributing to a reduction in energy consumption. The paper proposes one of such algorithms that develops the idea of clustering wireless sensor networks in order to reduce the power consumption of sensor nodes by transferring some of the functions to the head nodes of the clusters. Unlike the well-known adaptive clustering algorithm with low energy consumption LEACH, the proposed algorithm is based on swarm intelligence and allows choosing not only the head nodes of clusters in the current round of functioning of the wireless sensor network, but also promising nodes that become heads of clusters in subsequent rounds. If we consider that one cycle of the wireless sensor network consists of a certain predetermined number of rounds, then the procedure for searching for cluster heads can be performed not at the beginning of each round, but only at the beginning of each cycle of the wireless sensor network. It is shown that the determination of the heads of wireless sensor network clusters in the future allows to reduce the total energy consumption and thereby increase the duration of the network life cycle. The advantage of adding the bee swarm algorithm to the wireless sensor network clustering procedure is demonstrated in terms of such indicators as the time of death of the first sensor node, the dependence of the number of functioning nodes on the network operation time and the data packet delivery coefficient. The wireless sensor network clustering procedure with the addition of the bee swarm algorithm to select cluster heads for the future can be useful when deploying a wireless sensor network in real applications.

Since routing is a fundamental operation in all types of networks, ensuring routing security is a necessary requirement to guarantee the success of routing operation. Securing routing task gets more challenging as the target network lacks an infrastructure-based routing operation. This infrastructure-less nature that invites a multihop routing operation is one of the main features of wireless sensor networks that raises the importance of secure routing problem in these networks. Moreover, the risky environment, application criticality, and resources limitations and scarcity exhibited by wireless sensor networks make the task of secure routing much more challenging. All these factors motivate researchers to find novel solutions and approaches that would be different from the usual approaches adopted in other types of networks. The purpose of this chapter is to provide a comprehensive treatment of the routing security problem in wireless sensor networks. The discussion flow of the problem in this chapter begins with an overview on wireless sensor networks that focuses on routing aspects to indicate the special characteristics of wireless sensor networks from routing perspective. The chapter then introduces the problem of secure routing in wireless sensor networks and illustrates how crucial the problem is to different networking aspects. This is followed by a detailed analysis of routing threats and attacks that are more specific to routing operation in wireless sensor networks. A research-guiding approach is then presented to the reader that analyzes and criticizes different techniques and solution directions for the secure routing problem in wireless sensor network. This is supported by state-of-the-art and familiar examples from the literature. The chapter finally concludes with a summary and future research directions in this field.

Since routing is a fundamental operation in all types of networks, ensuring routing security is a necessary requirement to guarantee the success of routing operation. Securing routing task gets more challenging as the target network lacks an infrastructure-based routing operation. This infrastructure-less nature that invites a multihop routing operation is one of the main features of wireless sensor networks that raises the importance of secure routing problem in these networks. Moreover, the risky environment, application criticality, and resources limitations and scarcity exhibited by wireless sensor networks make the task of secure routing much more challenging. All these factors motivate researchers to find novel solutions and approaches that would be different from the usual approaches adopted in other types of networks. The purpose of this chapter is to provide a comprehensive treatment of the routing security problem in wireless sensor networks. The discussion flow of the problem in this chapter begins with an overview on wireless sensor networks that focuses on routing aspects to indicate the special characteristics of wireless sensor networks from routing perspective. The chapter then introduces the problem of secure routing in wireless sensor networks and illustrates how crucial the problem is to different networking aspects. This is followed by a detailed analysis of routing threats and attacks that are more specific to routing operation in wireless sensor networks. A research-guiding approach is then presented to the reader that analyzes and criticizes different techniques and solution directions for the secure routing problem in wireless sensor network. This is supported by state-of-the-art and familiar examples from the literature. The chapter finally concludes with a summary and future research directions in this field.

Electric power grid contains three main subsystems, i.e., power generation, power transmission & distribution, and customer facilities. Recently, wireless sensor networks (WSNs) have been considered as a promising technology that can enhance all these three subsystems, making WSNs an important component of the smart grid. However, environmental noise and interference from nonlinear electric power equipments and fading in harsh smart grid environments, makes reliable communication a challenging task for single-channel WSNs for smart grid applications. To improve network capacity in smart grid environments, multi-channel WSNs might be the preferred solution while achieving simultaneous transmissions through multiple channels. In this paper, the performance of multi-channel WSNs is investigated for different spectrum environments of smart power grid, e.g., 500kV outdoor substation, main power control room and underground network transformer vaults. In addition, we also introduce potential applications of multi-channel WSNs along with the related technical challenges. Here, our goal is to envision potential advantages and applications of multi-channel WSNs for smart grid and motivate the research community to further explore this promising research area.

This paper presents a survey on the current state-of-the-art in Wireless Sensor Network (WSN) Operating Systems (OSs). In recent years, WSNs have received tremendous attention in the research community, with applications in battlefields, industrial process monitoring, home automation, and environmental monitoring, to name but a few. A WSN is a highly dynamic network because nodes die due to severe environmental conditions and battery power depletion. Furthermore, a WSN is composed of miniaturized motes equipped with scarce resources e.g., limited memory and computational abilities. WSNs invariably operate in an unattended mode and in many scenarios it is impossible to replace sensor motes after deployment, therefore a fundamental objective is to optimize the sensor motes’ life time. These characteristics of WSNs impose additional challenges on OS design for WSN, and consequently, OS design for WSN deviates from traditional OS design. The purpose of this survey is to highlight major concerns pertaining to OS design in WSNs and to point out strengths and weaknesses of contemporary OSs for WSNs, keeping in mind the requirements of emerging WSN applications. The state-of-the-art in operating systems for WSNs has been examined in terms of the OS Architecture, Programming Model, Scheduling, Memory Management and Protection, Communication Protocols, Resource Sharing, Support for Real-Time Applications, and additional features. These features are surveyed for both real-time and non-real-time WSN operating systems.

With the success of wireless sensor networks (WSNs), traditional engineering and infrastructure industries are starting to develop solutions using WSN technologies. One of the main challenges of designing and developing WSNs for industrial monitoring and control is satisfying their strict reliability requirements. In this paper, we present a network-level reliability model, namely, end-to-end data delivery reliability (E2E-DDR), for estimating and optimizing the reliability performance of WSNs. In the E2E-DDR model, a framework is presented for capturing the mapping function between the packet reception ratio, background noise, and received signal strength (RSS). We use an alpha-stable distribution to accurately represent the background noise and a modified log-normal path loss model to more realistically describe the RSS. We also report a comprehensive performance evaluation performed by applying the E2E-DDR model in a real-world case study to estimate the network-level reliability and optimize the WSN deployment parameters. Note to Practitioners —The goal of this paper is to improve the estimation and optimization of the network-level reliability performance of industrial wireless sensor networks (WSNs) to satisfy their strict control requirements. In harsh industrial application scenarios, many factors affect radio link quality, such as RF transmit power, communication distance, and random background noise. Existing link quality estimation approaches mainly focus on providing a smoothed estimation of radio link quality without any solution for optimizing communication reliability to satisfy certain requirements. This paper suggests a new approach in which WSN nodes are used to measure and estimate the parameters of the scenario in which the nodes will be deployed and then to estimate and optimize the worst case reliability (which is a lower-bound value rather than a smoothed value) to ensure that the network is qualified. Through a real-world case study, we demonstrate how to estimate the lower bound on reliability and how to optimize the reliability by computing the maximum deployment distance between nodes as an example. The experiments suggest that this approach is feasible.

It is a difficult endeavor to realistically evaluate the performance of wireless sensor network (WSN) protocols. Generic network simulators are often used, but they tend to rely on synthetic models. Because WSN performance can be affected by many subtle features, these simulators lack a certain level of realism. The most realistic performance assessment is to implement the protocol in question and observe its performance in a real world deployment. This approach, however, is time consuming, costly, and makes the direct comparison of various protocols nearly impossible. We believe there exists a need to evaluate the real-world performance of WSN protocols in a controlled and repeatable fashion. To that end, we enable trace based WSN simulation by first enhancing an existing WSN profiler that automates the collection of connectivity traces and the generation of statistical link properties. We then present a trace based WSN simulator built on the discrete event simulator SimPy using the standard Python. The use of the high level language Python allows new WSN protocols to be rapidly prototyped and evaluated under the real-world conditions captured by the WSN profiler. To validate the premise that our simulation results closely model the real-world performance of the same protocol, we present a thorough performance analysis of the modern WSN collection tree protocol (CTP). Our approach enables the creation and use of a WSN trace database collected from various deployment environments. Such a database could be used to both fairly and more realistically benchmark existing WSN protocols and provide timely feedback on the real-world performance of protocols still in the development process.

Currently, there are many research studies which focus on analyzing the performance of Wireless Sensor Networks (WSNs) in several fields such as MAC protocols, routing protocols. In this paper we present an analytical model to evaluate the performance of WSNs based on the RSSI in the application layer. This model utilizes the available RSSI in most popular sensor nodes for analysis and considers the impact of several practical parameters such as data rate, routing information or packet acknowledgements. The use of empirical RSSI as the input can help the model to precisely reflect the real performance of deployed sensor networks. In order to carry out the validation of the model, a live WSN is setup to get the real data to input the proposed analytical model and the simulator for comparison. The results from real test-bed, simulation and analysis show that the proposed analytical model is precise enough and can be applied in estimating performance of WSNs.