Current key sharing techniques rely on the hardness of solving a solvable, but complex, mathematical problem. This entails, in Information Theoretical sense, the encryption key is not secret, it can be found by solving the underlying mathemat-ical problem. Sensitive data we encrypt today using traditional techniques can be recorded by malicious parties and be deciphered in the future whenever improved hacking techniques and supporting computing technology permit. Information Theory proves the existence of methods for sharing of encryption keys that are unconditionally secure, but does not show how to bring such theoretical results to practical use. One of the central information theoretical approaches to key sharing is based on exploiting common randomness. This theoretical result states that if two dependent random variables, $A$ and B, are available at Alice and Bob, then, by communicating through a public channel between $A$ and B, it is possible to securely share a key of size I(A;B). To bridge the gap between theory and practice, one needs a method to generate two sets of dependent random variables, one at Alice‘s side and the other one at the Bob’ side, as well as a method to extract two identical keys from these dependent random variables. This article presents a novel technique to achieve this goal over the Internet. Dependent random variables are generated by measuring packet travel times between Alice and Bob, and error-free key extraction from dependent random variables is realized by using a randomized Low Density Parity Check Code (LDPC). Through looping of packets between Alice and Bob, the mutual information between random variables is increased. Finally, methods are presented to measure the likelihood values required in decoding the underlying LDPC. It is shown that the key rate is approximately equal to $0.5\log_{9}(4L^{2}/(4L-1))\approx 0.5\log_{2}(L)$ where $L$ is the number of round trips (loops). Test results (based on measurements between distant nodes over the Internet) are presented, demonstrating the feasibility of the proposed technique. The proposed method is implemented entirely in software (through high-level programming, e.g., using C-language, at the application layer). This operation does not require modifying the underlying network.
In this paper, we aim to analyze the algebraic structure of repeated-root quasi-cyclic codes of length $p^{k}{n}\ell$ and index $\ell$ over the finite field $\mathbb{F}_{q}$ , where $k$ is a positive integer, $q=p^{\alpha}$ and $(n,p)=1$ . For this purpose, a quasi-cyclic code over $\mathbb{F}_{q}$ is regarded as a linear code over an auxiliary ring. By introducing a ring isomorphism, we provide a one-to-one correspondence between this class of quasi-cyclic codes and nonrepeated-root $(1-u)$ . quasi-twisted codes of length $n\ell$ and index $\ell$ over the chain ring $\mathbb{F}_{q}+u\mathbb{F}_{q}+\cdots+u^{p^{k}-1}\mathbb{F}_{q}$ , where $u^{p^{k}}=0$ . Our approach enables us to extend the results regarding non-repeated-root quasi-twisted codes over rings to repeated-root quasi-cyclic codes over finite fields. To illustrate the effectiveness of our method, we provide examples that demonstrate how it simplifies the structure of this class of codes.
Based on the analysis of the data link layer buffer queue state and the impact of application layer data flows on the physical layer of wireless communication nodes, this paper analyzes the restriction of communication link delay on the transmit power of nodes. Building on this analysis, a cross-layer optimization strategy is proposed with the goal of maximizing energy efficiency (EE), which selects the optimal adaptive modulation and coding (AMC) switching threshold and optimal power selection while considering the constraints of successful packet transmission rate and communication link delay. Finally, simulations are conducted to demonstrate the significant EE advantages of the proposed cross-layer optimization strategy.
The expansion of internet of vehicles (IoV) to host new applications for end users (EUs), such as extended reality (XR) and in-vehicle streaming services, is restrained by the computing capacity available at the EUs locally. To accelerate IoV expansion and abate the computation capacity limitation at the EU side, edge computing (EC) has exploded in recent years. In this work, we devise a workframe to optimize task offloading (TO) and results caching from an EU to an edge network made of two EC nodes with the objective of queuing delay risk assessment to minimize task dropping and maximize earned task credit by the primary EC node. We first formulate an integer non-linear programming (INLP) problem. Then, an algorithm based on the genetics algorithm (GA) is proposed to solve the problem. Our results show that the proposed algorithm achieves the best performance in terms of average task execution time, dropping rate, and earned credit by the primary EC node compared to traditional task offloading methods.
When designing routing schemes for opportunistic networks (OppNets), node's buffer congestion is one of the major concerns. Most buffer management schemes for OppNets are reactive ones, which require that more messages be transmitted. In this paper, a Buffer Occupancy Estimation Model (denoted BOEM) for OppNets is proposed, which uses the Chernoff's bound to estimate the expected buffer occupancy of any node without involving the communication of messages. Through simulations using both the realistic and synthetic mobility models, the effectiveness of the proposed model is proven, showing that it can significantly reduce the overall quantity of messages sent while leading to reduce node's buffer congestion.
