Vladimir Dyo

Evolution and sustainability of a wildlife monitoring sensor network

As sensor network technologies become more mature, they are increasingly being applied to a wide variety of applications, ranging from agricultural sensing to cattle, oceanic and volcanic monitoring. Significant efforts have been made in deploying and testing sensor networks resulting in unprecedented sensing capabilities. A key challenge has become how to make these emerging wireless sensor networks more sustainable and easier to maintain over increasingly prolonged deployments. In this paper, we report the findings from a one year deployment of an automated wildlife monitoring system for analyzing the social co-location patterns of European badgers (Meles meles) residing in a dense woodland environment. We describe the stages of its evolution cycle, from implementation, deployment and testing, to various iterations of software optimization, followed by hardware enhancements, which in turn triggered the need for further software optimization. We report preliminary descriptive analyses of a subset of the data collected, demonstrating the significant potential our system has to generate new insights into badger behavior. The main lessons learned were: the need to factor in the maintenance costs while designing the system; to look carefully at software and hardware interactions; the importance of a rapid initial prototype deployment (this was key to our success); and the need for continuous interaction with domain scientists which allows for unexpected optimizations.

WILDSENSING: Design and deployment of a sustainable sensor network for wildlife monitoring

The increasing adoption of wireless sensor network technology in a variety of applications, from agricultural to volcanic monitoring, has demonstrated their ability to gather data with unprecedented sensing capabilities and deliver it to a remote user. However, a key issue remains how to maintain these sensor network deployments over increasingly prolonged deployments. In this article, we present the challenges that were faced in maintaining continual operation of an automated wildlife monitoring system over a one-year period. This system analyzed the social colocation patterns of European badgers (Meles meles) residing in a dense woodland environment using a hybrid RFID-WSN approach. We describe the stages of the evolutionary development, from implementation, deployment, and testing, to various iterations of software optimization, followed by hardware enhancements, which in turn triggered the need for further software optimization. We highlight the main lessons learned: the need to factor in the maintenance costs while designing the system; to consider carefully software and hardware interactions; the importance of rapid prototyping for initial deployment (this was key to our success); and the need for continuous interaction with domain scientists which allows for unexpected optimizations.

Efficient Node Discovery in Mobile Wireless Sensor Networks

Energy is one of the most crucial aspects in real deployments of mobile sensor networks. As a result of scarce resources, the duration of most real deployments can be limited to just several days, or demands considerable maintenance efforts (e.g., in terms of battery substitution). A large portion of the energy of sensor applications is spent in node discovery as nodes need to periodically advertise their presence and be awake to discover other nodes for data exchange. The optimization of energy consumption, which is generally a hard task in fixed sensor networks, is even harder in mobile sensor networks, where the neighbouring nodes change over time. In this paper we propose an algorithm for energy efficient node discovery in sparsely connected mobile wireless sensor networks. The work takes advantage of the fact that nodes have temporal patterns of encounters and exploits these patterns to drive the duty cycling. Duty cycling is seen as a sampling process and is formulated as an optimization problem. We have used reinforcement learning techniques to detect and dynamically change the times at which a node should be awake as it is likely to encounter other nodes. We have evaluated our work using real human mobility traces, and the paper presents the performance of the protocol in this context.

A node discovery service for partially mobile sensor networks

Wireless Sensor Networks (WSNs) are challenging types of networks where resources can be scarce. In particular, battery is often a very limited resource and the radio interface is the culprit for most of the energy consumption. This makes any discovery of other sensors a difficult task, less cumbersome if sensors are fixed but crucial if (some) sensors start being mobile (such as in wildlife monitoring projects with tagged animals). In this paper we propose a middleware offering node discovery for partially mobile wireless sensor networks, where fixed nodes (sinks), deployed in the environment to monitor the movement of entities, detect those patterns with low power consumption. The approach is based on various machine learning techniques which allows for learning and adapting the wake up strategy of the sinks dynamically. We also report on the evaluation of the approach through simulation and use of real movement traces.

Middleware design for integration of sensor network and mobile devices

Integration of sensor networks with mobile devices can provide additional flexibility and functionality for a variety of applications and can have a significant practical potential. Designing applications for such integrated networks is a difficult task. The paper discusses the key research issues associated with such integration and our approach to solve these problems by designing a middleware architecture for integration of sensornets with mobile devices.

Adaptive distributed indexing for spatial queries in sensor networks

Sensor networks have opened new horizons and opportunities for a variety of environmental monitoring, surveillance and healthcare applications. One of the major tasks of sensor networks is the distributed collection and processing of sensor readings over extended periods of time. We propose an energy-efficient hierarchical indexing approach for spatial data in sensor networks. Our indexing technique allows roaming users to navigate through sensor networks distributed over large geographical areas and to pose spatial queries about the location of the data in the network. The major challenge in designing such indexes is the minimization of the total amount of traffic needed to create and maintain the indexes, which is a function of region activity and the actual query rates. Given the dynamic character of the setting, these parameters might in fact change during the network operation, calling for a very adaptive solution.

Wildlife and environmental monitoring using RFID and WSN technology

Wireless Sensor Networks enable scientists to collect information about the environment with a granularity unseen before, while providing numerous challenges to software designers. Since sensor devices are often powered by small batteries, which take considerable effort to replace, it is of major importance to use energy carefully. We present two efficient ways of extending the lifetime of such systems: 1. an adaptive duty cycling protocol and 2. an adaptive data management protocol. Further, we present some details of our deployed sensor network in Wytham Woods, Oxfordshire.

Harvesting energy from ambient radio signals: A load of hot air?

Harvesting energy from ambient radio signals is claimed to hold much innovation potential. Advances in ultra-low power electronics, an appetite for reducing the environmental footprint of technology and the business need for enabling new applications such as sensing in inaccessible locations are widely believed to be drivers. We review these drivers, and recent technological advances to reveal what potential there is by harvesting energy from ambient radio signals. Particular attention is given to the possibility of powering smart meters in this way.

Wireless Magnetic Sensor Network for Road Traffic Monitoring and Vehicle Classification

Abstract Efficiency of transportation of people and goods is playing a vital role in economic growth. A key component for enabling effective planning of transportation networks is the deployment and operation of autonomous monitoring and traffic analysis tools. For that reason, such systems have been developed to register and classify road traffic usage. In this paper, we propose a novel system for road traffic monitoring and classification based on highly energy efficient wireless magnetic sensor networks. We develop novel algorithms for vehicle speed and length estimation and vehicle classification that use multiple magnetic sensors. We also demonstrate that, using such a low-cost system with simplified installation and maintenance compared to current solutions, it is possible to achieve highly accurate estimation and a high rate of positive vehicle classification.

Performance analysis of opportunistic relaying and opportunistic hybrid incremental relaying over fading channels

In this study, the authors develop and present a comprehensive analysis of two opportunistic cooperative relaying schemes for long term evolution (LTE)-advanced networks operating over generalised-K and Nakagami-m fading channels. They present and compare the performance of opportunistic relaying (OR) and opportunistic hybrid automatic repeat request incremental relaying (OHIR). They analyse performance in terms of the average symbol error rate for both conventional OR and OHIR LTE-advanced networks with the radio channel modelled as composite generalised-K fading (encompassing both fading and shadowing) and Nakagami-m fading channels. They also analyse the outage probability for OR operating over these channels. Both the theoretical analysis and simulations confirm that for conventional OR LTE-advanced networks operating over composite generalised-K fading channels, a diversity order of k(N + 1) is achieved when shadowing is more severe than fading, and a diversity order of m(N + 1) is achieved when fading is more severe than shadowing (where k and m represent the generalised-K distribution shape parameters and N represents the number of candidate relays for the OR selection). The simulation results confirm the accuracy of the analytical expressions developed in this study. It is evident from the theoretical analysis and simulations that, for a similar quality of service as that for OR, OHIR not only reduces the amount of required radio resources but also maintains the full diversity order.