Performance analysis: Securing SIP on multi-threaded/multi-core proxy server using public keys on Diffie-Hellman (DH) in single and multi-server queuing scenarios.

The rapid replacement of PSTN with VOIP networks indicates the definitive phase-out of the PBX/PABX with smartphone-based VOIP technology that uses WLAN connectivity for local communication; however, security remains a key issue, regardless of the communication coverage area. Session initiation protocol (SIP) is one of the most widely adopted VOIP connection establishment protocols but requires added security. On the Internet, different security protocols, such as HTTPS (SSL/TLS), IPSec, and S/MIME, are used to protect SIP communication. These protocols require sophisticated infrastructure and some pose a significant overhead that may deteriorate SIP performance. In this article, we propose the following: i) avoid using Internet bandwidth and complex Internet protocols for local communication within an organization, but harness WLAN connectivity, ii) use multi-threaded or multicore computer systems to handle concurrent calls instead of installing hardware-based SIP servers, and iii) run each thread in a separate core. Cryptography is a key tool for securely transmitting confidential data for long- and short-range communication, and the Diffie-Hellman (DH) protocol has consistently been a popular choice for secret key exchanges. Primarily, used for symmetric key sharing, it has been proven effective in generating public/private key pairs, sharing public keys securely over public channels, and subsequently deriving shared secret keys from private/public keys. This key exchange scheme was proposed to safeguard VOIP communication within WLANs, which rely on the SIP for messaging and multimedia communication. For ensuring an efficient implementation of SIP, the system was rigorously analyzed using the M/M/1 and M/M/c queuing models. We analyze the behavior of SIP servers with queuing models with and without end-to-end security and increase users' trust in SIP security by providing a transparent sense of end-to-end security as they create and manage their private and public keys instead of relying on the underlying SIP technology. This research implements instant messaging, voice conversation, and secret key generation over DH while implementing and observing the role of multi-threading in multiqueue systems that serve incoming calls. By increasing the number of threads from one to two, the SIP response time improved from 20.23809 to 0.08070 min at an arrival rate of 4250 calls/day and a service rate of three calls/min. Similarly, by adding one to seven threads, the queue length was reduced by four calls/min. Implementing secure media streaming and reliable AES-based signaling for session confidentiality and integrity introduces a minor 8-ms tradeoff in SIP service performance. However, the advantages of implementing added security outweigh this limitation.

An Energy-Efficient Routing Protocol with Reinforcement Learning in Software-Defined Wireless Sensor Networks.

The enormous increase in heterogeneous wireless devices operating in real-time applications for Internet of Things (IoT) applications presents new challenges, including heterogeneity, reliability, and scalability. To address these issues effectively, a novel architecture has been introduced, combining Software-Defined Wireless Sensor Networks (SDWSN) with the IoT, known as the SDWSN-IoT. However, wireless IoT devices deployed in such systems face limitations in the energy supply, unpredicted network changes, and the quality of service requirements. Such challenges necessitate the careful design of the underlying routing protocol, as failure to address them often results in constantly disconnected networks with poor network performance. In this paper, we present an intelligent, energy-efficient multi-objective routing protocol based on the Reinforcement Learning (RL) algorithm with Dynamic Objective Selection (DOS-RL). The primary goal of applying the proposed DOS-RL routing scheme is to optimize energy consumption in IoT networks, a paramount concern given the limited energy reserves of wireless IoT devices and the adaptability to network changes to facilitate a seamless adaption to sudden network changes, mitigating disruptions and optimizing the overall network performance. The algorithm considers correlated objectives with informative-shaped rewards to accelerate the learning process. Through the diverse simulations, we demonstrated improved energy efficiency and fast adaptation to unexpected network changes by enhancing the packet delivery ratio and reducing data delivery latency when compared to traditional routing protocols such as the Open Shortest Path First (OSPF) and the multi-objective Q-routing for Software-Defined Networks (SDN-Q).

An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks.

In wireless sensor networks, tree-based routing can achieve a low control overhead and high responsiveness by eliminating the path search and avoiding the use of extensive broadcast messages. However, existing approaches face difficulty in finding an optimal parent node, owing to conflicting performance metrics such as reliability, latency, and energy efficiency. To strike a balance between these multiple objectives, in this paper, we revisit a classic problem of finding an optimal parent node in a tree topology. Our key idea is to find the best parent node by utilizing empirical data about the network obtained through Q-learning. Specifically, we define a state space, action set, and reward function using multiple cognitive metrics, and then find the best parent node through trial and error. Simulation results demonstrate that the proposed solution can achieve better performance regarding end-to-end delay, packet delivery ratio, and energy consumption compared with existing approaches.