Zuriati Ahmad Zulkarnain

FuGeF: A Resource Bound Secure Forwarding Protocol for Wireless Sensor Networks

Resource bound security solutions have facilitated the mitigation of spatio-temporal attacks by altering protocol semantics to provide minimal security while maintaining an acceptable level of performance. The Dynamic Window Secured Implicit Geographic Forwarding (DWSIGF) routing protocol for Wireless Sensor Network (WSN) has been proposed to achieve a minimal selection of malicious nodes by introducing a dynamic collection window period to the protocol’s semantics. However, its selection scheme suffers substantial packet losses due to the utilization of a single distance based parameter for node selection. In this paper, we propose a Fuzzy-based Geographic Forwarding protocol (FuGeF) to minimize packet loss, while maintaining performance. The FuGeF utilizes a new form of dynamism and introduces three selection parameters: remaining energy, connectivity cost, and progressive distance, as well as a Fuzzy Logic System (FLS) for node selection. These introduced mechanisms ensure the appropriate selection of a non-malicious node. Extensive simulation experiments have been conducted to evaluate the performance of the proposed FuGeF protocol as compared to DWSIGF variants. The simulation results show that the proposed FuGeF outperforms the two DWSIGF variants (DWSIGF-P and DWSIGF-R) in terms of packet delivery.

An Efficient Framework Model for Optimizing Routing Performance in VANETs

Routing in Vehicular Ad hoc Networks (VANET) is a bit complicated because of the nature of the high dynamic mobility. The efficiency of routing protocol is influenced by a number of factors such as network density, bandwidth constraints, traffic load, and mobility patterns resulting in frequency changes in network topology. Therefore, Quality of Service (QoS) is strongly needed to enhance the capability of the routing protocol and improve the overall network performance. In this paper, we introduce a statistical framework model to address the problem of optimizing routing configuration parameters in Vehicle-to-Vehicle (V2V) communication. Our framework solution is based on the utilization of the network resources to further reflect the current state of the network and to balance the trade-off between frequent changes in network topology and the QoS requirements. It consists of three stages: simulation network stage used to execute different urban scenarios, the function stage used as a competitive approach to aggregate the weighted cost of the factors in a single value, and optimization stage used to evaluate the communication cost and to obtain the optimal configuration based on the competitive cost. The simulation results show significant performance improvement in terms of the Packet Delivery Ratio (PDR), Normalized Routing Load (NRL), Packet loss (PL), and End-to-End Delay (E2ED).

A Spawn Mobile Agent Itinerary Planning Approach for Energy-Efficient Data Gathering in Wireless Sensor Networks

Mobile agent (MA), a part of the mobile computing paradigm, was recently proposed for data gathering in Wireless Sensor Networks (WSNs). The MA-based approach employs two algorithms: Single-agent Itinerary Planning (SIP) and Multi-mobile agent Itinerary Planning (MIP) for energy-efficient data gathering. The MIP was proposed to outperform the weakness of SIP by introducing distributed multi MAs to perform the data gathering task. Despite the advantages of MIP, finding the optimal number of distributed MAs and their itineraries are still regarded as critical issues. The existing MIP algorithms assume that the itinerary of the MA has to start and return back to the sink node. Moreover, each distributed MA has to carry the processing code (data aggregation code) to collect the sensory data and return back to the sink with the accumulated data. However, these assumptions have resulted in an increase in the number of MA’s migration hops, which subsequently leads to an increase in energy and time consumption. In this paper, a spawn multi-mobile agent itinerary planning (SMIP) approach is proposed to mitigate the substantial increase in cost of energy and time used in the data gathering processes. The proposed approach is based on the agent spawning such that the main MA is able to spawn other MAs with different tasks assigned from the main MA. Extensive simulation experiments have been conducted to test the performance of the proposed approach against some selected MIP algorithms. The results show that the proposed SMIP outperforms the counterpart algorithms in terms of energy consumption and task delay (time), and improves the integrated energy-delay performance.

Enhanced Tight Finite Key Scheme for Quantum Key Distribution (QKD) Protocol to Authenticate Multi-Party System in Cloud Infrastructure

This research is introducing an enhanced tight finite key scheme for quantum key distribution (QKD) protocol to authenticate multi-party system in cloud infrastructure. The main attraction is to provide a secure channel between a cloud client to establish a connection among them by applying the theories from Von Neumann and Shannon entropies and also Shors algorithm. By generalizing these theories we will produce enhanced tight finite key scheme for quantum key distribution (QKD) protocol to authenticate multi-party system in cloud infrastructure. Hence we are using quantum channel and also quantum key distribution (QKD) together with BB84 protocol replacing common channel to distribute the key. We are proposing an authentication of multi-party Quantum Key Distribution (MQKD) protocol using an enhanced tight finite key scheme because it will involve a number of parties in cloud infrastructure. Significant of this research is to reduce the possibility of losing a private key by producing a high efficient key rate and attack resilient.

Improving utility accrual scheduling algorithm for adaptive real-time System

In this paper, we proposed a preemptive utility accrual scheduling (or PUAS) algorithm to further extend the functionalities of General Utility Scheduling (or GUS) algorithm proposed by Peng Li [1]. Both of these algorithms are developed for adaptive real-time system environment where untoward effects such as deadline misses and overloads are tolerable. The proposed algorithm focused on an independent task model, which works on deadline constraints that are specified by using step time/utility functions (or TUFs). The proposed algorithm improves the GUS algorithm by preempting the tasks that GUS abort due to its lower PUD, lowering abortion ratio which in turn increase accrued utility. This met the scheduling objective of maximizing utility, which are achieved thru completion of all tasks. Results from our simulations showed that the proposed algorithm PUAS achieved higher utility and lower abortion ration compared to GUS algorithm. This in effect, produced a much lower average response time, making PUAS more efficient in time-critical application domain.

A Review on Heuristics for Addition Chain Problem: Towards Efficient Public Key Cryptosystems

Field exponentiation and scalar multiplication are the pillars of and the most computationally expensive operations in the public key cryptosystems. Optimizing the operation is the key to the efficiency of the systems. Analogous to the optimization is solving addition chain problem. In this study, we survey from the onset of the addition chain problem to the state-of-the-art heuristics for optimizing it, with the view to identifying fundamental issues that when addressed renders the heuristics most optimal mean of minimizing the two operations in various public key cryptosystems. Thus, our emphasis is specifically on the heuristics: Their various constraints and implementations efficiencies. We present possible ways forwards toward the optimal solution for the addition chain problem that can be efficiently applied for optimal implementation of the public key cryptosystems.

Multi-mobile agent itinerary planning algorithms for data gathering in wireless sensor networks: A review paper

Recently, wireless sensor networks have employed the concept of mobile agent to reduce energy consumption and obtain effective data gathering. Typically, in data gathering based on mobile agent, it is an important and essential step to find out the optimal itinerary planning for the mobile agent. However, single-agent itinerary planning suffers from two primary disadvantages: task delay and large size of mobile agent as the scale of the network is expanded. Thus, using multi-agent itinerary planning overcomes the drawbacks of single-agent itinerary planning. Despite the advantages of multi-agent itinerary planning, finding the optimal number of distributed mobile agents, source nodes grouping, and optimal itinerary of each mobile agent for simultaneous data gathering are still regarded as critical issues in wireless sensor network. Therefore, in this article, the existing algorithms that have been identified in the literature to address the above issues are reviewed. The review shows that most of the algorithms used one parameter to find the optimal number of mobile agents in multi-agent itinerary planning without utilizing other parameters. More importantly, the review showed that theses algorithms did not take into account the security of the data gathered by the mobile agent. Accordingly, we indicated the limitations of each proposed algorithm and new directions are provided for future research.

TruFiX: A Configurable Trust-Based Cross-Layer Protocol for Wireless Sensor Networks

The cross-layering concept has enabled flexibility in sensor communication by decreasing the level of modularity through inter-layer information exchange. This has improved adaptability, reliability, and efficiency in the communication process. This is principally so, because the inter-layer information is utilized to enable the selection of nodes that are perceived to foster efficient communication. However, despite these numerous achievements, the cross-layering concept suffers immensely as a result of security attacks, which prey on nodes utilized for data forwarding. In this paper, we propose T-XLM, a trust-based cross-layering framework to provide minimal defense against security attacks. The framework introduces a fuzzy-based trust estimation mechanism, which is used to formulate imprecise empirical knowledge that is utilized for reputation building in the nodes to ensure secure forwarding and reliable delivery of data. We further proposed trust-based fuzzy implicit cross-layer protocol (TruFiX), a T-XLM inspired protocol which utilizes multiple parameters pulled through inter-layer information exchange to mitigate the effects of security threats in a network. Using extensive simulation experiments, TruFiX was compared with resource bound security solution (RBSS)-based protocols, which also achieved minimal security by altering their routing semantics. The conducted experiments evaluated the security performance of the protocols and the results showthat the proposed TruFiX significantly outperforms the RBSS-based protocols in terms of packet delivery.

A Forwarding Strategy for DWSIGF Routing Protocol

Routing protocols in Wireless Sensor Network (WSN) are responsible for propagating and coordinating of information transfer from one end of the network to the other. Dynamic Window Secured Implicit Geographic Forwarding (DWSIGF) is a robust, cross layer, security bound routing protocol that propagates information in a multi-hop network using the greedy and random forwarding strategies. These strategies are known for their poor resistivity to interference and erratic behavior in path selection. In this paper, we propose a forwarding strategy that uses an optimal distance to mitigate these problems. The optimal distance is computed based on the path loss coefficient and energy dissipated in the hardware (sensor). Extensive simulations have been conducted to evaluate the performance of the proposed approach. The results illustrate that the proposed approach performs better than the compared strategies in terms of packet delivery ratio and energy consumption.

Enhanced Preemptive Global Utility Accrual Real Time Scheduling Algorithms in Multicore Environment

This paper proposed an efficient real time scheduling algorithm using global scheduling paradigm running in multicore environment known as Global Preemptive Utility Accrual Scheduling (GPUAS) algorithm. The existing TUF/UA multiprocessor scheduling algorithms known as Greedy-Global Utility Accrual (G-GUA) and Non Greedy-Global Utility Accrual (NG-GUA) algorithms is seen to overlook the efficiency on its task scheduling algorithm. These algorithms have adapted the task migration attribute considering the load balancing problem in multi core platform. The existing PUAS uniprocessor scheduling algorithm is mapped into the multicore scheduling environment that consists of the global scheduling schemes considering the migration attribute of the executed tasks. The main principal of global scheduling is that it allows the executed tasks to migrate from one processor to the other processors whenever a scheduling event occurs in the system. The proposed GPUAS algorithm inherits the characteristics of PUAS in uniprocessor where it can preempt the highest PUD task at any event that occurs in the system. In this research, the proposed GPUAS algorithm enhanced the existing NG-GUA and G-GUA algorithms. The developed simulator has derived the set of parameter, events and performance metrics according to a detailed analysis of the base model. The proposed GPUAS algorithm achieved the highest accrued utility for the entire load range. The proposed GPUAS algorithm is more efficient than the existing algorithms, producing the highest accrued utility ratio and less abortion ratio making it more suitable and efficient for real time application domain.