Time-critical, wireless IoT applications have been drawing increasing attention lately. The most characterizing feature of such applications is that a packet has to be delivered within a certain deadline. Not meeting the deadline could result in severe consequences. Jamming attacks utilize the shared nature of the wireless medium to corrupt transmitted packets. Although a corrupted packet could be retransmitted several times until successful delivery, this would add latency and hence could lead to missing the deadline. In this paper, we propose a jamming detection scheme that uses the packet transmission time as a statistic to make detection decisions. The key insight behind our proposed scheme is that, a long transmission/retransmission time for a certain packet indicates an abnormal condition, such as jamming. Therefore, we devise an optimal transmission-time threshold that, when exceeded, a jammer is detected. Unlike most existing detection schemes where, in case of a detection error, retransmission could continue until the deadline is reached, our scheme aims to detect the jammer earlier than the deadline so that the remaining time (until the deadline) could be utilized in retransmitting the packet over a safe channel. The proposed detection scheme is a general framework that can be applied to many situations. After conducting a thorough analysis, we apply the proposed early-stop jamming detection framework to the Distributed Coordinated Function (DCF) medium access mechanism specified by the 802.11 standard. Our simulation results show significant performance gains achieved by the proposed scheme.

In this paper, we propose a channel allocation algorithm for heterogeneous multi-cell cognitive radio 5G networks. The proposed algorithm considers channel-availability and channel-demand heterogeneity across CR cells. We use the theory of continuous-time Markov chains to develop mathematical expressions for channel availability and channel demand. Both expressions are incorporated in a binary integer program to find the optimal channel allocation. Further, we equip the proposed channel allocation algorithm with a novel interference domain identification scheme to detect interference range overlaps between cognitive radio cells. This scheme is essential for channel allocation, as it plays an important role in avoiding inter-cell interference. Our simulation results show significant performance gains compared to existing algorithms overlooking channel-availability. Furthermore, we use the theory of Dantzig-Wolfe decomposition to develop a framework for transforming centralized channel allocation algorithms into distributed ones. Through simulation experiments, we demonstrate the efficacy of the proposed framework.

The predominant use of today's networks is content access and distribution. Network Coding (NC) is an innovative technique that has potential to improve the efficiency of multicast content distribution over multihop Wireless Mesh Networks (WMNs) by allowing intermediate Forwarding Nodes (FNs) to encode and then forward data packets. Practical protocols are needed to realize the benefits of the NC technique. However, the existing NC-based multicast protocols cannot accurately determine the minimum number of coded packets that a FN should send in order to ensure successful data delivery to the destinations, so that many redundant packets are injected into the network, leading to performance degradation. In this paper, we propose HopCaster, a novel reliable multicast protocol that incorporates network coding with hop-by-hop transport. HopCaster completely eliminates the need for estimating the number of coded packets to be transmitted by a FN, and avoids redundant packet transmissions. It also effectively addresses the challenges of heterogeneous multicast receivers. Moreover, a cross-layer multicast rate adaptation mechanism is proposed, which enables HopCaster to optimize multicast throughput by dynamically adjusting wireless transmission rate based on the changes in the receiver population and channel conditions during the course of multicasting a coded data chunk. Our evaluations show that HopCaster significantly outperforms the existing NC-based multicast protocols

In this paper, we propose a highly-reliable transmission strategy based on exploiting channel diversity for Internet-of-Things (IoT) networks equipped with cognitive radio (CR) capabilities (referred to as CR-IoT networks). Specifically, we investigate the channel-assignment problem with the objective of minimizing the number of assigned channels for each CR transmission subject to link-reliability, data rate and success probability constraints. We use the theory of continuous-time Markov chains to derive mathematical expressions for channel availability and reliability. These expressions are employed in a binary linear program model to formulate the channel assignment optimization problem, which is sub-optimally solved using a polynomial-time sequential fixing procedure. Theoretical performance analysis of the proposed channel assignment scheme is derived in terms of a lower bound on the achievable probability of successful packet transmission. Through simulation experiments, we demonstrate significant performance enhancement compared to existing reliability-unaware CR channel assignment algorithms.

In this article, we derive a closed-form expression for the probability of successful packet transmission (PSPT) over Rayleigh fading channels in cognitive radio networks (CRNs). The derived expression is novel, as it does not require prior channel state information. This is achieved by employing a probabilistic model for the packet transmission time, avoiding the less realistic deterministic model that is widely used in the literature. Furthermore, the derived expression allows trading accuracy with computational complexity. Our expression is computationally efficient with linear computational complexity. Simulation results demonstrate high accuracy in evaluating the PSPT, with controllable computational complexity.