Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

A pragmatic approach to area coverage in hybrid wireless sensor networks

Success of Wireless Sensor Networks (WSN) largely depends on whether the deployed network can provide desired area coverage with acceptable network lifetime. In hostile or harsh environments such as enemy territories in battlefields, fire or chemical spills, it is impossible to deploy the sensor nodes in a predeter- mined regular topology to guarantee adequate coverage. Random deployment is thus more practical and feasible for large target areas. On the other hand, random deployment of sensors is highly susceptible to the occurrence of coverage holes in the target area. A potential solution for enhancing the existing coverage achieved by random deployments involves the use of mobility capable sensors that would help fill the coverage holes. This thesis seeks to address the problem of determining the current coverage achieved by the non-deterministic deployment of static sensor nodes and subsequently enhancing the coverage using mobile sensors. The main contributions of this dissertation are the design and evaluation of MAPC (Mobility Assisted Probabilistic Coverage), a distributed protocol for ensuring area coverage in hybrid wireless sensor networks. The primary contribution is a pragmatic approach to sensor coverage and maintenance that we hope would lower the technical barriers to its field deployment. Most of the assumptions made in the MAPC protocol are realistic and implementable in real-life applications e.g., practical boundary estimation, coverage calculations based on a realistic sensing model, and use of movement triggering thresholds based on real radio characteristics etc. The MAPC is a comprehensive three phase protocol. In the first phase, the static sensors calculate the area coverage using the Probabilistic Coverage Algorithm (PCA). This is a deviation from the idealistic assumption used in the binary detection model, wherein a sensor can sense accurately within a well defined (usually circular) region. Static sensors execute the PCA algorithm, in a distributed way, to identify any holes in the coverage. In the second phase, MAPC scheme moves the mobile nodes in an optimal manner to fill these uncovered locations. For different types of initial deployments, the proposed movement algorithms consume only 30-40% of the energy consumed by the basic virtual force algorithm. In addition, this thesis addresses the problem of coverage loss due to damaged and energy depleted nodes. The problem has been formulated as an Integer Linear Program and implementable heuristics are developed that perform close to optimal solutions. By replacing in-operational nodes in phase three, MAPC scheme ensures the continuous operation of the WSN. Experiments with real mote hardware were conducted to validate the boundary and coverage estimation part of the MAPC protocol. Extensive discrete event simulations (using NS2) were also performed for the complete MAPC protocol and the results demonstrate that MAPC can enhance and maintain the area coverage by efficiently moving mobile sensor nodes to strategic positions in the uncovered area

Smart Computing and Sensing Technologies for Animal Welfare: A Systematic Review

Animals play a profoundly important and intricate role in our lives today. Dogs have been human companions for thousands of years, but they now work closely with us to assist the disabled, and in combat and search and rescue situations. Farm animals are a critical part of the global food supply chain, and there is increasing consumer interest in organically fed and humanely raised livestock, and how it impacts our health and environmental footprint. Wild animals are threatened with extinction by human induced factors, and shrinking and compromised habitat. This review sets the goal to systematically survey the existing literature in smart computing and sensing technologies for domestic, farm and wild animal welfare. We use the notion of \emph{animal welfare} in broad terms, to review the technologies for assessing whether animals are healthy, free of pain and suffering, and also positively stimulated in their environment. Also the notion of \emph{smart computing and sensing} is used in broad terms, to refer to computing and sensing systems that are not isolated but interconnected with communication networks, and capable of remote data collection, processing, exchange and analysis. We review smart technologies for domestic animals, indoor and outdoor animal farming, as well as animals in the wild and zoos. The findings of this review are expected to motivate future research and contribute to data, information and communication management as well as policy for animal welfare