Launching an efficient participatory sensing campaign: A smart mobile device-based approach

PublishedJournal Article© 2015 ACM. Participatory sensing is a promising sensing paradigm that enables collection, processing, dissemination and analysis of the phenomena of interest by ordinary citizens through their handheld sensing devices. Participatory sensing has huge potential in many applications, such as smart transportation and air quality monitoring. However, participants may submit low-quality, misleading, inaccurate, or even malicious data if a participatory sensing campaign is not launched effectively. Therefore, it has become a significant issue to establish an efficient participatory sensing campaign for improving the data quality. This article proposes a novel five-tier framework of participatory sensing and addresses several technical challenges in this proposed framework including: (1) optimized deployment of data collection points (DC-points); and (2) efficient recruitment strategy of participants. Toward this end, the deployment of DC-points is formulated as an optimization problem with maximum utilization of sensor and then a Wise-Dynamic DC-points Deployment (WD3) algorithm is designed for high-quality sensing. Furthermore, to guarantee the reliable sensing data collection and communication, a trajectory-based strategy for participant recruitment is proposed to enable campaign organizers to identify well-suited participants for data sensing based on a joint consideration of temporal availability, trust, and energy. Extensive experiments and performance analysis of the proposed framework and associated algorithms are conducted. The results demonstrate that the proposed algorithm can achieve a good sensing coverage with a smaller number of DC-points, and the participants that are termed as social sensors are easily selected, to evaluate the feasibility and extensibility of the proposed recruitment strategies

Securing Multi-Layer Communications: A Signal Processing Approach

Security is becoming a major concern in this information era. The development in wireless communications, networking technology, personal computing devices, and software engineering has led to numerous emerging applications whose security requirements are beyond the framework of conventional cryptography. The primary motivation of this dissertation research is to develop new approaches to the security problems in secure communication systems, without unduly increasing the complexity and cost of the entire system. Signal processing techniques have been widely applied in communication systems. In this dissertation, we investigate the potential, the mechanism, and the performance of incorporating signal processing techniques into various layers along the chain of secure information processing. For example, for application-layer data confidentiality, we have proposed atomic encryption operations for multimedia data that can preserve standard compliance and are friendly to communications and delegate processing. For multimedia authentication, we have discovered the potential key disclosure problem for popular image hashing schemes, and proposed mitigation solutions. In physical-layer wireless communications, we have discovered the threat of signal garbling attack from compromised relay nodes in the emerging cooperative communication paradigm, and proposed a countermeasure to trace and pinpoint the adversarial relay. For the design and deployment of secure sensor communications, we have proposed two sensor location adjustment algorithms for mobility-assisted sensor deployment that can jointly optimize sensing coverage and secure communication connectivity. Furthermore, for general scenarios of group key management, we have proposed a time-efficient key management scheme that can improve the scalability of contributory key management from O(log n) to O(log(log n)) using scheduling and optimization techniques. This dissertation demonstrates that signal processing techniques, along with optimization, scheduling, and beneficial techniques from other related fields of study, can be successfully integrated into security solutions in practical communication systems. The fusion of different technical disciplines can take place at every layer of a secure communication system to strengthen communication security and improve performance-security tradeoff

USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS

Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications. The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases. Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential. Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings. Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people

Characterization of the fundamental properties of wireless CSMA multi-hop networks

A wireless multi-hop network consists of a group of decentralized and self-organized wireless devices that collaborate to complete their tasks in a distributed way. Data packets are forwarded collaboratively hop-by-hop from source nodes to their respective destination nodes with other nodes acting as intermediate relays. Existing and future applications in wireless multi-hop networks will greatly benefit from better understanding of the fundamental properties of such networks. In this thesis we explore two fundamental properties of distributed wireless CSMA multi-hop networks, connectivity and capacity. A network is connected if and only if there is at least one (multi-hop) path between any pair of nodes. We investigate the critical transmission power for asymptotic connectivity in large wireless CSMA multi-hop networks under the SINR model. The critical transmission power is the minimum transmission power each node needs to transmit to guarantee that the resulting network is connected aas. Both upper bound and lower bound of the critical transmission power are obtained analytically. The two bounds are tight and differ by a constant factor only. Next we shift focus to the capacity property. First, we develop a distributed routing algorithm where each node makes routing decisions based on local information only. This is compatible with the distributed nature of large wireless CSMA multi-hop networks. Second, we show that by carefully choosing controllable parameters of the CSMA protocols, together with the routing algorithm, a distributed CSMA network can achieve the order-optimal throughput scaling law. Scaling laws are only up to order and most network design choices have a significant effect on the constants preceding the order while not affecting the scaling law. Therefore we further to analyze the pre-constant by giving an upper and a lower bound of throughput. The tightness of the bounds is validated using simulations

Characterization of the fundamental properties of wireless CSMA multi-hop networks

A wireless multi-hop network consists of a group of decentralized and self-organized wireless devices that collaborate to complete their tasks in a distributed way. Data packets are forwarded collaboratively hop-by-hop from source nodes to their respective destination nodes with other nodes acting as intermediate relays. Existing and future applications in wireless multi-hop networks will greatly benefit from better understanding of the fundamental properties of such networks. In this thesis we explore two fundamental properties of distributed wireless CSMA multi-hop networks, connectivity and capacity. A network is connected if and only if there is at least one (multi-hop) path between any pair of nodes. We investigate the critical transmission power for asymptotic connectivity in large wireless CSMA multi-hop networks under the SINR model. The critical transmission power is the minimum transmission power each node needs to transmit to guarantee that the resulting network is connected aas. Both upper bound and lower bound of the critical transmission power are obtained analytically. The two bounds are tight and differ by a constant factor only. Next we shift focus to the capacity property. First, we develop a distributed routing algorithm where each node makes routing decisions based on local information only. This is compatible with the distributed nature of large wireless CSMA multi-hop networks. Second, we show that by carefully choosing controllable parameters of the CSMA protocols, together with the routing algorithm, a distributed CSMA network can achieve the order-optimal throughput scaling law. Scaling laws are only up to order and most network design choices have a significant effect on the constants preceding the order while not affecting the scaling law. Therefore we further to analyze the pre-constant by giving an upper and a lower bound of throughput. The tightness of the bounds is validated using simulations

Foundations of coverage algorithms in autonomic mobile sensor networks

Drones are poised to become a prominent focus of advances in the near future as hardware platforms manufactured via mass production become accessible to consumers in higher quantities at lower costs than ever before. As more ways to utilize such devices become more popular, algorithms for directing the activities of mobile sensors must expand in order to automate their work. This work explores algorithms used to direct the behavior of networks of autonomous mobile sensors, and in particular how such networks can operate to achieve coverage of a field using mobility. We focus special attention to the way limited mobility affects the performance (and other factors) of algorithms traditionally applied to area coverage and event detection problems. Strategies for maximizing event detection and minimizing detection delay as mobile sensors with limited mobility are explored in the first part of this work. Next we examine exploratory coverage, a new way of analyzing sensor coverage, concerned more with covering each part of the coverage field once, while minimizing mobility required to achieve this level of 1-coverage. This analysis is contained in the second part of this work. Extending the analysis of mobility, we next strive to explore the novel topic of disabled mobility in mobile sensors, and how algorithms might react to increase effectiveness given that some sensors have lost mobility while retaining other senses. This work analyzes algorithm effectiveness in light of disabled mobility, demonstrates how this particular failure mode impacts common coverage algorithms, and presents ways to adjust algorithms to mitigate performance losses. --Abstract, page iv

New Coding/Decoding Techniques for Wireless Communication Systems

Wireless communication encompasses cellular telephony systems (mobile communication), wireless sensor networks, satellite communication systems and many other applications. Studies relevant to wireless communication deal with maintaining reliable and efficient exchange of information between the transmitter and receiver over a wireless channel. The most practical approach to facilitate reliable communication is using channel coding. In this dissertation we propose novel coding and decoding approaches for practical wireless systems. These approaches include variable-rate convolutional encoder, modified turbo decoder for local content in Single-Frequency Networks, and blind encoder parameter estimation for turbo codes. On the other hand, energy efficiency is major performance issue in wireless sensor networks. In this dissertation, we propose a novel hexagonal-tessellation based clustering and cluster-head selection scheme to maximize the lifetime of a wireless sensor network. For each proposed approach, the system performance evaluation is also provided. In this dissertation the reliability performance is expressed in terms of bit-error-rate (BER), and the energy efficiency is expressed in terms of network lifetime