Mudasser Iqbal

LACON: localized autonomic configuration in pervasive sensor networks

The success of a pervasive sensor network (PSN) of ubiquitous loco-dynamic devices mainly depends on its ability to configure itself according to the environment and the available resources to achieve optimum performance. This self configuring property is indispensable as ad hoc devices are usually exposed to natural or man-made disasters due to the hostile deployment of such networks. The most common dynamics posing a reconfiguration requirement are addition, failure and replacement of critical component devices. In addition to self configuration, the network must be able to monitor these dynamics in an autonomous way in order to take proactive measures for smooth re-configuration of the network, with minimal degradation in quality of service (QoS) provision. The paper presents a comprehensive model for monitoring and reconfiguring sensor networks and provides measures to mitigate network impairments. The results confirm that the proposed self configuring model adopts optimized design parameters and maintains QoS even for highly ad hoc networks.

An Energy-Aware Dynamic Clustering Algorithm for Load Balancing in Wireless Sensor Networks

Energy efficient dynamic clustering offers a flexible paradigm to reconfigure the network in order tomaximise network’s life-time in resource constrained ad hoc sensor networks. The load profiles of parent nodes (PNs) can be used to define its current state as well as to predict potential failures caused by energy loss due to high loads on particular PNs. This paper proposes a novel dynamicclustering algorithm for load balanced routing based upon route efficiency. The algorithm exploits the pattern and load of traffic and energy dissipation rate of each node on the route to calculate the node and route efficiencies. The proposed algorithm maintains PNs in a state whereby the network life requirement is met by the PNs comprising the backbone of the network. Results prove that the proposed methodology balances the load effectively to meet the network life requirement while concomitantly preserving the network capacity.

Integrated Sensing and Diagnosis -- The next step in Real Time Patient Health Care

Recently, the health care industry has observed a growing demand of continuous monitoring of patients to timely diagnose the diseases. This is attributed to a remarkable growth in uncertain deaths caused by diseases like heart attack and malignant neoplasm. While the existing sensor network technology has been envisaged as a solution to the real time patient monitoring, the focus of such efforts has been to deploy a sensing network and passively deliver the physiological readings from patients to the hospital infrastructures. In this paper, we introduce a framework that addresses a drawback in existing experimental test beds: the lack of feedback from the diagnosis infrastructure to the sensor network so that the network can reconfigure itself to monitor the patient in a more useful manner, while concomitantly maximizing the network reliability, throughput and life.

A novel load balancing technique for proactive energy loss mitigation in ubiquitous networks

Any ad hoc ubiquitous network should be capable of taking proactive measures for smooth reconfiguration in the event of energy loss and a failure to key network Parent Nodes (PN). The overall objective of reconfiguration is to keep the network operating effectively for a prescribed time frame. The load profiles of PNs can be used to define its current state as well as to predict potential failures caused by energy loss due to high loads on particular PNs. This paper presents a new technique utilising historical load profile information about each PN to improve the estimate accuracy of its remaining lifetime before failure. A novel load balancing model is introduced that maintains PNs in a state whereby the time- to-live requirement is met by the PNs comprising the backbone of the network. Results prove that the proposed methodologies achieve the reconfiguration objectives as well as maintaining QoS, especially in high density ad hoc networks.

Ambient Cardiac Expert: A Cardiac Patient Monitoring System using Genetic and Clinical Knowledge Fusion

Cardiac patients can be regularly monitored using low cast sensor networks which can save many lives and valuable time of experts. This monitoring can be more effective if in addition to standard clinical parameters genetic information is used because of its ability to predict hereditary diseases like cardiac problems. Current clinical practices, however, only stress on physiological observation to predict heart failure rate which could miss the important information which could lead to fatal consequences. This paper presents Ambient Cardiac Expert (ACE) which combines physiological parameters observed using sensor networks with gene expression data to predict the heart failure rate. The system uses well established Support Vector Machines (SVM) for class prediction and uses Wrapper Evolutionary Algorithm based on Gaussian Estimation of Distribution Algorithm (EDA) to determine cardiac patients criticality. Results suggest that ACE can be successfully applied for cardiac patient monitoring and has ability to integrate the information from both clinical and genetic sources.

Dynamic symmetrical topology models for pervasive sensor networks

The success of pervasive computing environments using ubiquitous loco-dynamic sensing devices is very dependent upon the sensor deployment topology (DT) employed. This paper presents a systematic mathematical model for efficient sensor deployment and provides a comparison with other popular topologies. The model focuses upon blanket coverage of a surveillance area using a minimum number of sensing devices, with minimal infra-sensor overlapping to reduce collisions and co-existence problems. Simulation results are presented for the hexagonal, triangular and square grid topologies for various dimensions of surveillance area. The results confirm that the hexagonal model gives optimal performance in terms of requiring the minimal number of sensors. The paper also highlights the improved performance of ubiquitous wireless sensor networks when a hexagonal topology (HT) is used.

HUSEC: A heuristic self configuration model for wireless sensor networks

The Network State Beacons (NSB) exchange rate is the critical parameter in monitoring the dynamic state of any self-configuring ad hoc sensor network. Along with the network state and the overhead control traffic, it determines both the time and nature of any proactive reconfiguration activity that is necessary to counteract any degradation in network performance. While traditionally selected at random, this rate of beacon exchange significantly affects the accuracy with which the variations in network state are tracked. It is therefore imperative to adapt this rate to the variation in load profile and attempt to maintain its value within predefined bounds which can be optimised to keep the network operating quiescently, while conforming to its various Quality-of-Service (QoS) requirements. This paper presents a novel, heuristic self-configuration (HUSEC) model which addresses this optimisation problem. The simulation results confirm that the model reconfigures the network more effectively to not only achieve higher throughput, but also greater network integrity, with minimal control traffic and resource overheads.

Optimizing the beacon exchange rate for proactive autonomic configuration in ubiquitous MANETs

Proactive self-configuration is indispensable for MANETs like ubiquitous sensor networks (USNs), as component devices of the network are usually exposed to natural or man-made disasters due to the hostile deployment and ad hoc nature of the USNs. Network state beacons (NSBs) are exchanged among the key nodes of the network for crucial and effective monitoring of the network for steady state operation. The rate of beacon exchange (F/sub E/) and its contents, define the time and nature of the proactive action. Therefore it is very important to optimize these parameters to tune the functional response of the USN. This paper presents a comprehensive model for monitoring and proactively reconfiguring the network by optimizing the F/sub E/. The results confirm the improved throughput while maintaining QoS over longer periods of network operation.

A Cross-Layer Data Dissemination Protocol for Energy Efficient Sink Discovery in Wireless Sensor Networks

This paper proposes a cross-layer protocol for energy-aware routing in wireless sensor networks. The protocol combines the energy depreciation rate, node distance and neighbourhood information from physical layer together with TDMA schedules from the MAC layer and also network life requirements from the application layer, to effectively determine the most efficient routes to the base station. This cross layer efficiency measure is then used to form dynamic clusters that adapt to changing traffic and energy conditions so assisting to both control the transmission power and schedule the sleep-wake cycles of nodes for better energy utilisation. The proposed protocol is a recursive aggregation scheme that transforms a network-wide routing dissemination problem into a single-hop query protocol that aids nodes in making multi-hop routing decisions based solely upon the information provided by their single-hop neighbours. Results confirm that the proposed technique balances the load on forwarding nodes, adapts the MAC layer precisely to the routing layer and minimizes data delivery time for increased traffic and large scale networks.

Online Load Balancing for Energy-Aware Anycast Routing

Anycast routing offers a flexible paradigm for resource constrained, ad hoc sensor networks, where sensors can route data to any available server. The selection policy of servers in an anycast scenario could assist in keeping the network operating quiescently for a required time frame. This paper proposes a novel load balancing model to make anycast routing decisions based on route efficiency, and remaining life-times of the servers. The model makes use of the historical load and channel capacity profiles to accurately estimate the remaining life of each server. The results show that the proposed technique achieves effective reconfiguration performance to meet the network time-to-live requirement and also maintains better load-balance with anycast routing protocol