Chowdhury Sayeed Hyder

ARC: Adaptive Reputation based Clustering Against Spectrum Sensing Data Falsification Attacks

IEEE 802.22 is the first standard based on the concept of cognitive radio. It recommends collaborative spectrum sensing to avoid the unreliability of individual spectrum sensing while detecting primary user signals. However, it opens an opportunity for attackers to exploit the decision making process by sending false reports. In this paper, we address security issues regarding distributed node sensing in the 802.22 standard and discuss how attackers can modify or manipulate their sensing result independently or collaboratively. This problem is commonly known as spectrum sensing data falsification (SSDF) attack or Byzantine attack. To counter the different attacking strategies, we propose a reputation based clustering algorithm that does not require prior knowledge of attacker distribution or complete identification of malicious users. We provide an extensive probabilistic analysis of the performance of the algorithm. We compare the performance of our algorithm against existing approaches across a wide range of attacking scenarios. Our proposed algorithm displays a significantly reduced error rate in decision making in comparison to current methods. It also identifies a large portion of the attacking nodes and greatly minimizes the false detection rate of honest nodes.

Defense against Spectrum Sensing Data Falsification Attacks in Cognitive Radio Networks

IEEE 802.22 is the first standard based on the concept of cognitive radio. It recommends collaborative spectrum sensing to avoid the unreliability of individual spectrum sensing while detecting primary user signals. However, it opens an opportunity for attackers to exploit the decision making process by sending false reports. In this paper, we address security issues regarding distributed node sensing in the 802.22 standard and discuss how attackers can modify or manipulate their sensing result independently or collaboratively. This problem is commonly known as spectrum sensing data falsification (SSDF) attack or Byzantine attack. To counter the different attacking strategies, we propose a reputation based clustering algorithm that does not require prior knowledge of attacker distribution or complete identification of malicious users. We compare the performance of our algorithm against existing approaches across a wide range of attacking scenarios. Our proposed algorithm displays a significantly reduced error rate in decision making compared to current methods. It also identifies a large portion of the attacking nodes and greatly minimizes the false detection rate of honest nodes.

Finding the Optimal Percentage of Cluster Heads from a New and Complete Mathematical Model on LEACH

Network lifetime is one of the important metrics that indicate the performance of a sensor network. Different techniques are used to elongate network lifetime. Among them, clustering is one of the popular techniques. LEACH (Low-Energy Adaptive Clustering Hierarchy) is one of the most widely cited clustering solutions due to its simplicity and effectiveness. LEACH has several parameters that can be tuned to get better performance. Percentage of cluster heads is one such important parameter which affects the network lifetime significantly. At present it is hard to find the optimum value for the percentage of cluster head parameter due to the absence of a complete mathematical model on LEACH. A complete mathematical model on LEACH can be used to tune other LEACH parameters in order to get better performance. In this paper, we formulate a new and complete mathematical model on LEACH. From this new mathematical model, we compute the value for the optimal percentage of cluster heads in order to increase the network lifetime. Simulation results verify both the correctness of our mathematical model and the effectiveness of computing the optimal percentage of cluster heads to increase the network lifetime.

Stable Sensor Network (SSN): A Dynamic Clustering Technique for Maximizing Stability in Wireless Sensor Networks

Stability is one of the major concerns in advancement of Wireless Sensor Networks (WSN). A number of applications of WSN require guaranteed sensing, coverage and connectivity throughout its operational period. Death of the first node might cause instability in the network. Therefore, all of the sensor nodes in the network must be alive to achieve the goal during that period. One of the major obstacles to ensure these phenomena is unbalanced energy consumption rate. Different techniques have already been proposed to improve energy consumption rate such as clustering, efficient routing, and data aggregation. However, most of them do not consider the balanced energy consumption rate which is required to improve network stability. In this paper, we present a novel technique, Stable Sensor Network (SSN) to achieve balanced energy consumption rate using dynamic clustering to guarantee stability in WSN. Our technique is based on LEACH (Low-Energy Adaptive Clustering Hierarchy), which is one of the most widely deployed simple and effective clustering solutions for WSN. We present three heuristics to increase the time before the death of first sensor node in the network. We devise the algorithm of SSN based on those heuristics and also formulate its complete mathematical model. We verify the efficiency of SSN and correctness of the mathematical model by simulation results. Our simulation results show that SSN significantly improves network stability period compared to LEACH and its best variant.

Towards a truthful online spectrum auction with dynamic demand and supply

In spectrum trading, secondary users bid for the spectrum units being made available by primary users. Auction theory has been widely applied to improve spectrum allocation in such spectrum trading scenarios. However, in contrast to reality, most of the research work assume either static user populations or static spectrum supply or both. In this work, we investigate a realistic dynamic auction environment where secondary users with diverse delay bounds arrive dynamically, and spectrum becomes available at random. We propose a priority ranking based online auction mechanism that prevents bidders from gaining advantage by misreporting information. We prove that the proposed auction mechanism is truthful and individual rational. We illustrate the properties of the mechanism in terms of spectrum utilization rate, bidder satisfaction rate, and average bidder utility through extensive simulations.

Bid and Time Truthful Online Auctions in Dynamic Spectrum Markets

The allocation of underutilized spectrum from primary users to secondary users in real time is likely the most promising avenue for advancing efficiency of spectrum use given the ever-increasing demand for transmission. Research in this area has focused on auctions to facilitate the distribution of spectrum, inducing truthful reporting by participants. However, most research has assumed a static or partially dynamic setting. These approaches are unable to capture that spectrum becomes available at random intervals as primary users’ needs vary across time; and, similarly, secondary users’ needs vary over time. Moreover, frequently there is flexibility regarding the time of transmission—with some transmissions being more urgent and time-sensitive than others. Therefore, existing research cannot be directly applied to such auction environments involving users with variable transmission deadlines, while preserving efficiency and truthfulness. In this paper, we present two truthful online auction mechanisms in dynamic spectrum markets that consider indefinite number of arrival of bidders with varying transmission deadlines and random availability of spectrum units over time. The first proposed mechanism SOADE assumes that the underlying distribution information of bidders and supplies is available. With that knowledge, the mechanism builds around a priority function that determines the rank of a bidder of winning spectrum at an auction considering its valuation, deadline, and uncertainty associated with dynamic arrival of bidders and spectrum availability. The second proposed mechanism xSOADE does not require any distribution knowledge. This mechanism applies bid monotonic spectrum allocation technique, determines the payment based on critical pricing, and enforces penalty rules to avoid manipulation. We prove that both the algorithms are truthful against bid and time-based cheating and individually rational through theoretical analysis and numerical simulations. Finally, we analyze the performance of these algorithms under different settings in terms of auction efficiency and auction revenue and demonstrate their effectiveness compared to prior work.

Exploiting cooperation for delay optimization in cognitive networks

In this paper, we exploit mutual cooperation between primary and secondary users to reduce delay in packet transmission in a cognitive network. More formally, we present a delay optimization problem based on the cooperation model and show that the problem is NP-Hard. The mutual benefit through cooperation is analyzed using a time dependent priority queueing system. Analytical simulation results are presented to validate the proposed cooperation model.

Enhancing reliability of real-time traffic via cooperative scheduling in cognitive radio networks

In this paper, we study the cooperation model to design and develop a system that improves the reception rate of delay sensitive packets in realtime applications. While making a cooperation decision, a user with real-time traffic must consider the queue size, packet deadline, the traffic rate, channel condition. Existing cooperation models do not take these factor into consideration and therefore, are not applicable to the improvement of reliability of real-time traffic. We formulate the cooperation model using a Markov decision process (MDP) which is NP-Hard. The MDP-based optimization problem of a single pair of primary and secondary user is analyzed to find the optimum transmission decision in different states of the network. Based on the findings of a single pair case, we develop a distributed cooperation algorithm where a user (primary or secondary) quantifies the immediate and future impact of cooperation and takes its decision accordingly. Extensive simulation is performed to evaluate the performance of the proposed cooperation algorithm which reveals its efficacy compared to the non-cooperative scheme.

Digging the innate reliability of wireless networked systems

Network reliability of wireless networks exhibits a prominent impact in successful advancement of the networking paradigm. A complete understanding of the network reliability demands its in-depth analysis, which is yet to be attempted in the literature. Therefore, we present a comprehensive study on the network reliability in this paper. Our step-by-step stochastic study, from node-level to network-level reliability, reveals a novel finding: the network reliability of a wireless network follows the Gaussian distribution in general. We validate the finding through exhaustive numerical simulation and ns-2 simulation.

DBLA: Distributed block learning algorithm for channel selection in Cognitive Radio Networks

In this paper, we study the distributed channel selection problem in a cognitive network. We consider a time varying channel environment where secondary users do not have any prior knowledge of primary transmission and independently learn about existing channels without any explicit communication with other users. To solve this problem, we address the impact of switching between channels during the learning process that is mostly ignored in literature. We propose a scalable distributed block learning algorithm that minimizes the switching cost, adapts to time varying channel conditions, and achieves logarithmic regret. Simulation results show that our algorithm performs significantly better than the existing ones.