Waqar Asif

Variable rate adaptive modulation (VRAM) for introducing small-world model into WSNs

Data communication has a strong impact on the design of a Wireless Sensor Network (WSN), since the data transmission energy cost is typically higher than the data processing cost. In order to reduce the data transmission cost, small world phenomenon is introduced into WSNs. Networks that do not have the small world structure can be converted to achieve a small world property by the addition of few extra links. The problem is that most large scale WSNs are inherently unstructured and a node has no precise information of the overall model of the network and thus has to rely on the knowledge of its neighbor. For this reason, in most unstructured networks, information is propagated using gossiping. In this paper, we exploit this information propagation mechanism and use Neighbor Avoiding Walk (NAW), where the information is propagated to node that has not been visited previously and which is not the neighbor of a previously visited node. Using this, a novel approach is presented, in which nodes with highest betweenness centrality form a long distance relay path by using a lower order modulation scheme and therefore resulting in a relatively reduced data rate, but maintaining the same bit error rate. Our empirical and analytical evaluations demonstrate that this leads to a significant reduction in average path length and an increase in node degree.

Topology Control for Harvesting Enabled Wireless Sensor Networks: A Design Approach

While there has been a lot of research on energy efficient topology control protocols destined for different applications, topology control has never been explored in the presence of harvesting enabled sensors. Largely, researchers in this domain have considered a fixed battery design. We argue that arrival of harvesting enabled sensors necessitates rethink of topology control. The objective of topology control in this context should not be to minimize the spent energy and maintain a reduced topology, but to maximize fault tolerance in the network and increase the sensing coverage region. In this work, we first describe a taxonomy of existing topology control schemes and analyze the impact of reduced topology over fault tolerance and sensing coverage. We then describe the necessity of new design parameters in the presence of harvest-able ambient energy. We also outline guiding principles for designing a harvesting enabled topology control scheme. To cater for whether such a scheme is feasible or not, an insight is also provided onto the solar energy availability from solar radiations for near perpetual operation—as an example of available ambient energy. Based on the insight gained from the solar radiations availability, we explain why new design parameters are required for performance measurement of harvesting enabled sensors. The mathematical and empirical findings reveal that the topology control strategies, which do not take into account harvesting opportunity, are unable to provide better results in terms of fault tolerance and sensing coverage.

Combined Banzhaf & Diversity Index (CBDI) for critical node detection

Critical node discovery plays a vital role in assessing the vulnerability of a computer network to malicious attacks and failures and provides a useful tool with which one can greatly improve network security and reliability. In this paper, we propose a new metric to characterize the criticality of a node in an arbitrary computer network which we refer to as the Combined Banzhaf & Diversity Index (CBDI). The metric utilizes a diversity index which is based on the variability of a nodes attributes relative to its neighbours and the Banzhaf power index which characterizes the degree of participation of a node in forming shortest paths. The Banzhaf power index is inspired from the theory of voting games in game theory. The proposed metric is evaluated using analysis and simulations. The criticality of nodes in a network is assessed based on the degradation in network performance achieved when these nodes are removed. We use several performance metrics to evaluate network performance including the algebraic connectivity which is a spectral metric characterizing the connectivity robustness of the network. Extensive simulations in a number of network topologies indicate that the proposed CBDI index chooses more critical nodes which, when removed, degrade network performance to a greater extent than if critical nodes based on other criticality metrics were removed.

Increasing user controllability on device specific privacy in the Internet of Things

With recent advancements in information technology more and more devices are integrated in the Internet of Things. These devices gather significant amount of private information pertinent to a user and while, in some cases it helps in improving the life style of an individual, in others it raises major privacy concerns. This trade-off between utility and privacy is highly dependent upon the devices in consideration and as the utility of the generated data increases, the privacy of an individual decreases. In this paper, we formulate a utility-privacy trade-off that enables a user to make appliance specific decisions as to how much data can be shared. This is achieved by parametrizing the degree of privacy allowed for each device and enabling the user to configure the parameter of each device. We use the smart metering application as the test case scenario for the proposed approach. We evaluate its performance using simulations conducted on the ECO data set. Our results indicate that, the proposed approach is successful in identifying appliances with an accuracy of 81.8% and a precision of 70.1%. In addition, it is demonstrated that device specific changes of the configuration parameters allow the degree of privacy achieved for the particular device and the utility to be well controlled, thus demonstrating the effectiveness of the proposed approach. Moreover, it is shown that, as expected, devices with higher power consumption contribute more to the overall privacy and utility achieved. A comparative study is also conducted and the proposed approach is shown to outperform the existing ElecPrivacy approach by producing a trace that is harder to identify, as reported after testing the Weiss’ and Baranski’s algorithm, both of which are well known Non-Intrusive Load Monitoring algorithms. Finally, it is demonstrated that the addition of noise, which is an integral part of the propose approach, can greatly improve performance.

CBDI: Combined Banzhaf & diversity index for finding critical nodes

Critical node discovery plays a vital role in assessing the vulnerability of a network to an abrupt change, such as an adversarial attack or human intervention. In this paper, we propose a new metric to characterize the criticality of a node in an arbitrary network which we refer to as the Combined Banzhaf & Diversity Index (CBDI). The metric utilizes a diversity index which is based on the variability of a nodes attributes relative to its neighbors and the Banzhaf Power Index which characterizes the degree of participation of a node in forming shortest paths. The Banzhaf power index is inspired from the theory of voting games in game theory. We evaluate the performance of the new metric using simulations. Our results indicate that in a number of network topologies, the proposed metric outperforms other proposals which have appeared in the literature. The proposed CBDI index chooses more critical nodes which, when removed, degrade network performance to a greater extent than if critical nodes based on other criticality metrics were removed.

Adding the Reliability on Tree Based Topology Construction Algorithms for Wireless Sensor Networks

Topology control is a technique used in wireless sensor networks to maximize energy efficiency and network lifetime. In previous literature, many tree based techniques have been proposed to save energy and increase the network lifetime. In tree based algorithms, the most promising solution is the formation of a network backbone, which serves on behalf of rest of the nodes in the network and therefore leading towards Connected Dominating Set (CDS) formulation. However, one imminent problem with all tree based solution is a compromise on network reliability. Therefore, to address reliability issues in tree based solutions, in this paper, we propose Poly3 which maintains cliques of size three in order to achieve network reliability on top of the CDS algorithm. This makes the network more robust to link removal. Our empirical and mathematical analysis reveals that Poly3 provides better reliability than algorithms of the same kind.

On the complexity of average path length for biological networks and patterns

Path length calculation is a frequent requirement in studies related to graph theoretic problems such as genetics. Standard method to calculate average path length (APL) of a graph requires traversing all nodes in the graph repeatedly, which is computationally expensive for graphs containing large number of nodes. We propose a novel method to calculate APL for graphs commonly required in the studies of genetics. The proposed method is computationally less expensive and less time-consuming compared to standard method.

Active energy management for harvesting enabled wireless sensor networks

In this work, we propose a novel control based scheme for energy management in wireless sensor networks with energy harvesting capability. The problem is viewed as a queue control problem where the objective is to maintain a reference energy level of the energy storage device. A validated nonlinear queueing model is considered and a robust nonlinear controller is considered whose convergence properties are established analytically. The proposed control algorithm incorporates predictions of the energy to be harvested which are generated using the recently proposed Accurate Solar Irradiance Prediction Model (ASIM). The combined control and prediction scheme is evaluated using analysis and simulations. Preliminary simulation results conducted on the NS-3 simulator demonstrate the ability of the scheme to achieve its design objective as the energy level oscillates about values close to the desired level.

Spectral partitioning for node criticality

Finding critical nodes in a network is a significant task, highly relevant to network vulnerability and security. We consider the node criticality problem as an algebraic connectivity minimization problem where the objective is to choose nodes which minimize the algebraic connectivity of the resulting network. Previous suboptimal solutions of the problem suffer from the computational complexity associated with the implementation of a maximization consensus algorithm. In this work, we use spectral partitioning concepts introduced by Fiedler, to propose a new suboptimal solution which significantly reduces the implementation complexity. Our approach, combined with recently proposed distributed Fiedler vector calculation algorithms enable each node to decide by itself whether it is a critical node. If a single node is required then the maximization algorithm is applied on a restricted set of nodes within the network. We derive a lower bound for the achievable algebraic connectivity when nodes are removed from the network and we show through simulations that our approach leads to algebraic connectivity values close to this lower bound. Similar behaviour is exhibited by other approaches at the expense, however, of a higher implementation complexity.

Energy and path aware clustering algorithm (EPAC) for mobile ad hoc networks

Node clustering is a technique that mitigates the change in topology in Ad hoc communication. It stabilizes the end to end communication path and maximizes the path life time. In SWARM communication, each cluster is assigned an objective and expected to complete it in the available resources. Most of the algorithms previously designed assume that the assignment of tasks can be done in any arbitrary manner and does not depend on the energy resources. In this work, we have emphasized that the number of nodes in a cluster is fundamentally related to the energy requirement of the objective. With the help of this new algorithm, we minimize energy consumption in a cluster by improving the mechanism for selecting objective, depending upon the amount of energy present at the nodes of that cluster.