Collaborative signal and information processing for target detection with heterogeneous sensor networks

In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce high performance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behaviour and capacity, target behaviour and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield

Feedback Control Goes Wireless: Guaranteed Stability over Low-power Multi-hop Networks

Closing feedback loops fast and over long distances is key to emerging applications; for example, robot motion control and swarm coordination require update intervals of tens of milliseconds. Low-power wireless technology is preferred for its low cost, small form factor, and flexibility, especially if the devices support multi-hop communication. So far, however, feedback control over wireless multi-hop networks has only been shown for update intervals on the order of seconds. This paper presents a wireless embedded system that tames imperfections impairing control performance (e.g., jitter and message loss), and a control design that exploits the essential properties of this system to provably guarantee closed-loop stability for physical processes with linear time-invariant dynamics. Using experiments on a cyber-physical testbed with 20 wireless nodes and multiple cart-pole systems, we are the first to demonstrate and evaluate feedback control and coordination over wireless multi-hop networks for update intervals of 20 to 50 milliseconds.Comment: Accepted final version to appear in: 10th ACM/IEEE International Conference on Cyber-Physical Systems (with CPS-IoT Week 2019) (ICCPS '19), April 16--18, 2019, Montreal, QC, Canad

Wireless sensor networks using network coding for structural health monitoring

Wireless Sensor Networks (WSNs) have been deployed for the purpose of structural health monitoring (SHM) of civil engineering structures, e.g. bridges. SHM applications can potentially produce a high volume of sensing data, which consumes much transmission power and thus decreases the lifetime of the battery-run networks. We employ the network coding technique to improve the network efficiency and prolong its lifetime. By increasing the transmission power, we change the node connectivity and control the number of nodes that can overhear transmitted messages so as to hopefully realize the capacity gain by use of network coding. In Chapter 1, we present the background, to enable the reader to understand the need for SHM, advantages and drawbacks of WSNs and potential the application of network coding techniques has. In Chapter 2 we provide a review of related research explaining how it relates to our work, and why it is not fully applicable in our case. In Chapter 3, we propose to control transmission power as a means to adjust the number of nodes that can overhear a message transmission by a neighbouring node. However, too much of the overhearing by high power transmission consumes aggressively limited battery energy. We investigate the interplay between transmission power and network coding operations in Chapter 4. We show that our solution reduces the overall volume of data transfer, thus leading to significant energy savings and prolonged network lifetime. We present the mathematical analysis of our proposed algorithm. By simulation, we also study the trade-offs between overhearing and power consumption for the network coding scheme. In Chapter 5, we propose a methodology for the optimal placement of sensor nodes in linear network topologies (e.g., along the length of a bridge), that aims to minimise the link connectivity problems and maximise the lifetime of the network. Both simple packet relay and network coding are considered for the routing of the collected data packets towards two sink nodes positioned at both ends of the bridge. Our mathematical analysis, verified by simulation results, shows that the proposed methodology can lead to significant energy saving and prolong the lifetime of the underlying wireless sensor network. Chapter 6 is dedicated to the delay analysis. We analytically calculate the gains in terms of packet delay obtained by the use of network coding in linear multi-hop wireless sensor network topologies. Moreover, we calculate the exact packet delay (from the packet generation time to the time it is delivered to the sink nodes) as a function of the location of the source sensor node within the linear network. The derived packet delay distribution formulas have been verified by simulations and can provide a benchmark for the delay performance of linear sensor networks. In the Chapter 7, we propose an adaptive version of network coding based algorithm. In the case of packet loss, nodes do not necessary retransmit messages as they are able to internally decide how to cope with the situation. The goal of this algorithm is to reduce the power consumption, and decrease delays whenever it can. This algorithm achieves the delay similar to that of three-hop direct-connectivity version of the deterministic algorithm, and consumes power almost like one-hop direct-connectivity version of deterministic algorithm. In very poor channel conditions, this protocol outperforms the deterministic algorithm both in terms of delay and power consumption. In Chapter 8, we explain the direction of our future work. Particularly, we are interested in the application of combined TDMA/FDMA technique to our algorithm.Open Acces

Integrated placement and routing of relay nodes for fault-tolerant hierarchical sensor networks

In two-tiered sensor networks, using higher-powered relay nodes as cluster heads has been shown to lead to further improvements in network performance. Placement of such relay nodes focuses on achieving specified coverage and connectivity requirements with as few relay nodes as possible. Existing placement strategies typically are unaware of energy dissipation due to routing and are not capable of optimizing the routing scheme and placement concurrently. We, in this thesis, propose an integrated integer linear program (ILP) formulation that determines the minimum number of relay nodes, along with their locations and a suitable communication strategy such that the network has a guaranteed lifetime as well as ensuring the pre-specified level of coverage (ks) and connectivity (kr). We also present an intersection based approach for creating the initial set of potential relay node positions, which are used by our ILP, and evaluate its performance under different conditions. Experimental results on networks with hundreds of sensor nodes show that our approach leads to significant improvement over existing energy-unaware placement schemes

Review of Optimization Problems in Wireless Sensor Networks

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