Mohsin Memon

Clustering and fault tolerance for target tracking using wireless sensor networks

Using wireless sensor networks to track a moving object provided a practical solution to a wide variety of applications including, for example, wild life, military operations, intruder tracking and monitoring in indoor office buildings. While much work has been done in this area, failures are not considered in most of the existing solutions. However, failures have to be handled carefully in target tracking applications because of their unpredictable and dynamic nature of communication, such as sensor energy depletion, severe environment conditions, unstable communication links and malicious attacks. Traditional approaches of fault tolerance are not well suited to address these new challenges. Therefore the authors propose a novel fault-tolerant target tracking (FTTT) protocol based on clustering. Also, the results of an investigation in terms of performance overheads and scalable nature of the FTTT protocol via comparative simulations with LEACH are discussed. Overall, in all the cases, FTTT consumes at least 25% less energy than LEACH.

Energy-aware fault-tolerant clustering scheme for target tracking wireless sensor networks

Over the last few years, the deployment of wireless sensor networks (WSNs) has been fostered in diverse applications. WSN has great potential for a variety of domains ranging from scientific experiments to commercial applications. Due to the deployment of WSNs in dynamic and unpredictable environments, these applications must have potential to cope with variety of faults. This paper proposes an energy-aware fault-tolerant clustering protocol for target tracking applications termed as the Fault-Tolerant Target Tracking (FTTT) protocol. The identification of redundant nodes (RNs) makes sensor node (SN) fault tolerance plausible and the clustering endorsed recovery of sensors supervised by a faulty cluster head (CH). The FTTT protocol intends two steps of reducing energy consumption: first, by identifying RNs in the network; secondly, by restricting the numbers of SNs sending data to the CH. Simulations validate the scalability and low power consumption of the FTTT protocol in comparison with LEACH protocol.

Restrain from pervasive logging employing geo-temporal policies

Life logging has been a prominent research concern in recent years with the invention of wearable life capture gadgets and it has played a significant role in some situations such as helping Alzheimer disease patients. However, at the same time, it has raised privacy concerns among ordinary people. At present, life log devices are pervasively capturing information, including people in the vicinity without their consent. This will produce a great concern in the future if the majority of people come to have life log devices that record continuously what is happening around. In this paper, we propose a mechanism to restrict people from capturing a person in their personal digital diaries in real time by introducing Geo-temporal privacy framework. Furthermore, the system ensures that the unwilling party is not revealed to the life logging system users and privacy is sustained when the Geo-temporal framework discontinues the log activity after an encounter with the reluctant party. The prototype is developed on an Android-based smart phone that works as a life log device with a policy controller. The phone is connected to an Infrared Transmitter/Receiver with an interface board, for identifying human proximity.

Model Checking of a Target Tracking Protocol for Wireless Sensor Networks

Dense collection of tiny Sensor Nodes (SNs), equipped with a variety of sensors are able to sense events, compute and communicate the estimations to the end user, form a distributed Wireless Sensor Network (WSN). The advances in WSNs have made it plausible to appraise critical aspects of object tracking WSNs that are continuous monitoring of object track and fault tolerance. This paper presents probabilistic performance evaluation of clustering, redundancy reduction, target tracking and fault tolerance phases of Fault-Tolerant Target Tracking (FTTT) protocol. This type of formal modeling with limited number of SNs helps to plan a scalable network with specific constraints related to the application, for example, object tracking quality, life time of the network, energy consumption, latency, QoS and network throughput.

Ensuring Privacy during Pervasive Logging by a Passerby

Pervasive logging devices capture everything along with the public nearby without their consent, thus, pos- sibly troubling people who prefer their privacy. This has issues for privacy and, furthermore, the widespread use of such logging devices may affect peoples behavior, as they may feel uncomfortable that they are constantly being mon- itored. People may wish to have some control over the lifelogging devices of others and, in this article, we describe a framework to restrict anonymous logging, unless explicitly permitted. Our privacy framework allows the user of a log- ging device to define privacy policies controlling when, where and who to restrict from logging them. Moreover, it is possible to select which type of logging sensors to apply these restrictions. Evaluation results show that this approach is a practical method of configuring privacy settings and restricting pervasive devices from logging.

Modelling and symmetry reduction of a target-tracking protocol using wireless sensor networks

To achieve precise modelling of real-time systems stochastic behaviours are considered which lead towards probabilistic modelling. Probabilistic modelling has been successfully employed in wide array of application domains including, for example, randomised distributed algorithms, communication, security and power management protocols. This study is an improvement over our previous work, which was based on the probabilistic analysis of a cluster-based fault tolerant target-tracking protocol (FTTT) using only grid-based sensor nodes arrangement. Probabilistic modelling is chosen for the analysis of FTTT protocol to facilitate benefits of symmetry reduction in conjunction with modelling. It is believed that for the first time correctness of the simplified version of a target-tracking protocol is verified by developing its continuous-time Markov chain (CTMC) model using symbolic modelling language. The proposed probabilistic model of a target-tracking wireless sensor networks will help to analyse the phases of FTTT protocol on a limited scale with finite utilisation of time. There are three main contributions of this study; first consideration of synchronised events between the modules, second, random placement of sensor nodes is taken into account in addition to grid-based sensor node arrangement, third one is the reduction in state space size through symmetry reduction technique, which also facilitates to analyse a larger size network. Symmetry reduction on Probabilistic Symbolic Model (PRISM) checker models is performed by PRISM-symm and the generic representatives in PRISM (GRIP) tool. Modelling of FTTT protocol is proved better with the usage of PRISM-symm after comparing the results of PRISM model, PRISM-symm and GRIP.

Sharing life experiences with friends based on individual's locality

There are numerous events that transpire in ones life. Since, the need to make the most of these life logs is the ultimate goal of life logging; therefore, we present an approach to share ones life experiences with friends. The proposed mechanism assists ones friends who are involved in identical situations with shared life logs, provided that they check-in the same location where the logs were originally produced. A user of the system can capture and share images, audio recordings, QR-code or barcode as well as real objects via the prototype life log device. The friends in response can send feedback about the shared data to support the sharer of the log. The prototype application is developed on an Android-based smart phone that works as a life log device and employs Facebook and Dropbox API for fetching ones friends list and storing life log data in the cloud respectively.