Farshid Hassani Bijarbooneh

Cloud-Assisted Data Fusion and Sensor Selection for Internet of Things

The Internet of Things (IoT) is connecting people and smart devices on a scale that was once unimaginable. One major challenge for the IoT is to handle vast amount of sensing data generated from the smart devices that are resource-limited and subject to missing data due to link or node failures. By exploring cloud computing with the IoT, we present a cloud-based solution that takes into account the link quality and spatio-temporal correlation of data to minimize energy consumption by selecting sensors for sampling and relaying data. We propose a multiphase adaptive sensing algorithm with belief propagation (BP) protocol (ASBP), which can provide high data quality and reduce energy consumption by turning on only a small number of nodes in the network. We formulate the sensor selection problem and solve it using both constraint programming (CP) and greedy search. We then use our message passing algorithm (BP) for performing inference to reconstruct the missing sensing data. ASBP is evaluated based on the data collected from real sensors. The results show that while maintaining a satisfactory level of data quality and prediction accuracy, ASBP can provide load balancing among sensors successfully and preserves 80% more energy compared with the case where all sensor nodes are actively involved.

Optimising quality of information in data collection for mobile sensor networks

Wireless sensor networks have become increasingly popular for environmental and activity monitoring, such as temperature, pollution, parking space, traffic, and crowd monitoring. Mobile users can collect and visualise sensing data by communicating with wireless sensors along their walks using Bluetooth or NFC. They can also share the sensing data on the Internet through 3G or WiFi connectivity. Nevertheless, mobile users may not be able to collect all the data from the sensors due to limited contact times and batteries. It is crucial to collect data with a maximum amount of information from the available resources. In this paper, we tackle the problem by prioritising the sensing data to maximise the data utility considering the quality of information of the sensing data and the communication overhead. We formulate the optimisation problem and propose a greedy algorithm for clustering the sensors and scheduling the data collection. Our greedy algorithm coordinates the mobile users in the sensing field in order to avoid the collection of redundant sensing data. We evaluate the data utility and energy consumption of the proposed algorithm using real mobility traces from the North Carolina state fair. The results demonstrate that our algorithm can significantly improve data utility at low communication overhead compared with an existing algorithm.

ProFuN TG: A tool for programming and managing performance-aware sensor network applications

Sensor network macroprogramming methodologies such as the Abstract Task Graph hold the promise of enabling high-level sensor network application development. However, progress in this area is hampered by the scarcity of tools, and also because of insufficient focus on developing tool support for programming applications aware of performance requirements. We present ProFuN TG (Task Graph), a tool for designing sensor network applications using task graphs. ProFuN TG provides automated task mapping, sensor node firmware macrocompilation, application simulation, deployment, and runtime maintenance capabilities. It allows users to incorporate performance requirements in the applications, expressed through constraints on task-to-task dataflows. The tool includes middleware that uses an efficient flooding-based protocol to set up tasks in the network, and also enables runtime assurance by keeping track of the constraint conditions. We show that the adaptive task reallocation enabled by our approach can significantly increase application reliability while decreasing energy consumption: in a network with unreliable links, we achieve above 99.89 % task-to-task PDR while keeping the maximal radio duty cycle around 2.0 %.

A constraint programming approach for managing end-to-end requirements in sensor network macroprogramming

A constraint programming approach for managing end-to-end requirements in sensor network macroprogramming

Energy-Efficient Sensor Selection for Data Quality and Load Balancing in Wireless Sensor Networks

It is common to deploy stationary sensors in large geographical environments for monitoring purposes. In such cases, the monitored data are subject to data loss due to poor link quality or node failures. Fortunately, the sensing data are highly correlated both spatially and temporally. In this paper, we consider such networks in general, and jointly take into account the link quality estimates, and the spatio-temporal correlation of the data to minimise energy consumption by selecting sensors for sampling and relaying data. In particular, we propose a multi-phase adaptive sensing algorithm with belief propagation protocol (ASBP), which can provide high data quality and reduce energy consumption by turning on only a small number of nodes in the network. We explore the correlation of data, formulate the sensor selection problem and solve it using constraint programming (CP) and greedy search. Bayesian inference technique is used to reconstruct the missing sensing data. We show that while maintaining a satisfactory level of data quality and prediction accuracy, ASBP successfully provides load balancing among sensors and preserves 80% more energy compared to the case where all sensor nodes are actively involved.

An optimisation-based approach for wireless sensor deployment in mobile sensing environments

We consider a novel application in wireless sensor networks where mobile phones and wireless sensors can collaborate to collect sensing data. Although mobile phones can perform sensing at different locations, it is a challenge to provide stable sensing quality and availability over the entire area. One approach is to deploy stationary sensors at specific locations to maintain the sensing quality and availability. In this paper, we present a mathematical programming model to minimise the deployment cost by placing a minimum number of sensors at optimal locations. The problem is modelled by integer linear programming considering the sensing capabilities of both the mobile phones and wireless sensors. We evaluated the performance of our solution in terms of sensing quality, number of required sensors, and computation time. The results demonstrate that our approach satisfies the required sensing quality with optimal number of sensors in small sensing fields. It achieves near optimal solution with low computation time for large sensing fields.

Dynamic demand-capacity balancing for air traffic management using constraint-based local search : First results

Dynamic demand-capacity balancing for air traffic management using constraint-based local search : First results

Enabling Design of Performance-Controlled Sensor Network Applications through Task Allocation and Reallocation

Task Graph (ATaG) is a sensor network application development paradigm where the application is visually described by a graph where the nodes correspond to application-level tasks and edges correspond to data flows. We extend ATaG with the option to add non-functional requirements: constraints on end-to-end delay and packet delivery rate. Setting up these constraints at the design phase naturally leads to enabling run-time assurance at the deployment phase, when the conditions of the constraints are used as networks performance goals. We provide both run-time middleware that checks the conditions of these constraints and a central management unit that dynamically adapts the system by doing task reallocation and putting task copies on redundant nodes. Through extensive simulations we show that the system is efficient enough to enable adaptations within tens of seconds even in large networks.

Constraint programming for wireless sensor networks

In recent years, wireless sensor networks (WSNs) have grown rapidly and have had a substantial impact in many applications. A WSN is a network that consists of interconnected autonomous nodes that ...