Integrating AI inference into wireless sensing edge networks presents notable challenges due to limited resources, changing environments, and diverse devices. In this study, we proposed a novel resource allocation framework that enhances energy efficiency, reduces latency, and ensures fairness across distributed edge nodes for AI inference. The framework models a multi-objective optimization problem that reflects the interdependence of computation, communication, and energy at each device. We also develop a decentralized algorithm based on dual decomposition and projected gradient ascent, by using local data. The extensive simulations demonstrate that our proposed method reduces the average inference latency by 31.4% and energy consumption by 27.8% compared to the greedy and round-robin techniques. The system utility is improved by up to 59.2%, and fairness, measured using Jain’s index, remains within 8% of the ideal. Additionally, throughput analysis further confirms that our approach gains up to 49 tasks/sec, outperforming existing strategies by more than 40%. These findings show that the resource-aware AI inference approach is scalable, energy-efficient, and appropriate for real-time use in multi-user wireless edge networks. • Proposes a decentralized resource allocation framework for AI inference in wireless sensing edge networks. • Jointly optimizes latency, energy consumption, and fairness under dynamic workloads. • Employs a dual decomposition-based algorithm with minimal control signaling for scalability. • Achieves up to 31.4% lower latency and 27.8% lower energy use compared to greedy and round-robin schemes. • Maintains Jain’s fairness index above 0.95 while improving system utility by up to 59.2.

Implicit neural representations have recently demonstrated considerable potential in various applications, including video compression and reconstruction, owing to their rapid decoding speed and high adaptability. Based on the most advanced neural representation for Videos (NeRV), Expedite Neural Representation for Videos (E-NeRV) and Hybrid Neural Representation for Videos (H-NeRV) primarily boost performance by enhancing and broadening the NeRV network’s embedded input, whereas the NeRV module—the central component involved in video reconstruction—has attracted less attention. With a focus on high-frequency data in the frequency domain, this paper proposes a High-frequency Spectrum Hybrid Network (HFS-HNeRV), which adopts effective high-frequency data from the frequency domain to generate image details. Its core, HFS-HNeRV block, is a novel NeRV module, which adds the high-frequency spectrum convolution module (HFSCM) to the original one. This module extracts and emphasizes high-frequency features through the frequency domain attention mechanism, which not only provides superior performance, but also enhances the local detail recovery in video images. As an upgrade of the NeRV module, it has exceptional performance in terms of adaptability and versatility. It can conveniently substitute in a variety of current NeRV designs without requiring significant alterations to attain enhanced performance. Furthermore, this paper also introduces the High-frequency Spectrum (HFS) loss function to further mitigate the blurriness issue caused by the loss of high-frequency information during image generation. In the video compression task, the proposed HFS-HNeRV network outperformed NeRV, E-NeRV and HNeRV with an improvement of +5.68 dB, +4.46 dB, and +0.98 dB in reconstruction quality (PSNR), respectively.

In this paper, an active inductor based reconfigurable mixer has been illustrated for software defined radio. The proposed mixer has three stages: the common source configured mixer, the active inductor, and the current mirror section. The mixer is designed and simulated in Silterra 130-nm CMOS process and operates within 0.9-7 GHz band. It operates at 1.2V supply voltage and exhibits a maximum gain (S21) higher than 18 dB. The power dissipation of the mixer is 12.52mW with 3.5 dB noise Figure (NF) and 47.4 dBm IIP3. The proposed mixer covers less than 1mm2area. It is well suited for high performance, low cost, and power efficient radios.

It is desired to enhance traditional mechanical water meters with automatic meter reading (AMR) in the era of Internet of Things (IoT), In this paper, we propose and implement an add-on module using optical character recognition (OCR) to achieve that. The proposed module will be attached to a conventional mechanical water meter, capture and translate the register readings into plain text, which can be sent wirelessly to a database server. In addition, we propose a novel light-weight optical character recognition algorithm and test it in a prototype with embedded digital system. Experimental results show that the recognition accuracy can be as high as 98.1%, which indicates a promising candidate technology for AMR to revolutionise water meters. Finally, this technique can be applied to other mechanical meters with analogue number displays, paving the way for ubiquitous IoT.

Software defined networking (SDN) has emerged as a new technology to enhance the flexibility, resilience, and automated centralized management of a network. Recently several reports have identified possible vulnerabilities, which may affect its authenticity, availability, confidentiality and integrity. This paper analyzes several types of security issues in SDNs, especially on how to secure the communication between the control plane and the data plane. The state-of-the-art security protocol TLS in SDNs has been verified using the Scyther Tool. Two security schemes, namely TLSHPS and TLSIHP are proposed to improve the handshaking procedures of the TLS. Security analysis with the Scyther tool shows that both proposed schemes work well to prevent various cyber attacks.

The development of Vehicular Ad Hoc Networks (VANET) has brought many advantages to facilitate the deployment of the Intelligent Transportation System (ITS). However, without proper protection, VANETs can be vulnerable to severe cyber-attacks. This paper explores the threats to the VANETs and proposes a security scheme for VANETs with a Blockchain (VNB). Furthermore, the proposed VNB with Ethereum was developed. With a graphical user interface, experiments were conducted. For ad hoc communications, a vehicle can randomly select another vehicle, and VNB will authenticate the selected vehicle with the Blockchain and Trusted Authority (TA). Preliminary test results successfully proved that Blockchain can be the key technology to mitigate the security threats to VANETs.

How to achieve reliable routing has always been a major issue in the design of communication networks, among which mobile ad hoc networks (MANETs) possess the most adversarial networking environment due to the absence of fixed infrastructure, the nature of open transmission media and the dynamic network topology. These characteristics also make the design of routing protocols in MANETs become even more challenging. In this paper, we propose an evolutionary self-cooperative trust (ESCT) scheme that imitates human cognitive process and relies on trust-level information to prevent various routing disruption attacks. In this scheme, mobile nodes will exchange trust information and analyze received trust information based on their own cognitive judgment. Eventually, each node dynamically evolves its cognition to exclude malicious entities. The most attractive feature of ESCT is that they cannot compromise the system even if the internal attackers know how the security mechanism works. In this paper, we evaluate the performance of ESCT scheme under various routing disruption attack situations. Simulation results affirm that ESCT scheme promotes network scalability and ensures the routing effectiveness in the presence of routing disruption attackers in MANETs.

Non-orthogonal multiple access (NOMA) is a promising multiple access scheme to increase the system capacity for future fifth generation (5G) communication systems. However, NOMA with receivers employing conventional successive interference cancellation (SIC) technique requires a careful choice of target rates and power allocation coefficients in order to keep NOMA operational. In this paper, we represent downlink (DL) NOMA system in a novel way by proposing an equivalent transmission model, which is then utilized for interference modeling and estimation using stochastic geometry. Furthermore, we derived the closed-form expression for analyzing the outage performance of DL users. Numerical results show that with the proposed interference model and its application to estimate and remove the interference in the DL signal before decoding, NOMA not only outperforms its orthogonal multiple access (OMA) counterpart, but also manages to continue operating under conditions where NOMA with conventional SIC receiver suffers complete outage.

It is challenging to design a routing scheme that can successfully operate in the presence of adversarial environment in mobile ad hoc network (MANET), where several types of severe routing security threats can be easily employed against normal routing. Among them, active black hole attack is a typical one. Most existing defense schemes postulated that active black hole attackers have no knowledge about the internal prevention mechanisms of MANETs. However, due to the open and self-organized nature of MANET, it is necessary to assume that all nodes, including attackers, know how the security mechanism works. In this paper, we propose a distributed self-checking scheme that can effectively prevent various forms of active black hole attackers. Even the internal attackers who know the security mechanism cannot get any benefit to launch those attacks. Simulation results affirm that our proposed solution is robust and able to improve the packet delivery ratio significantly in an adversarial MANET environment as compared with other routing schemes. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, we propose a multihop medium access control (mMAC) scheme for time division duplexing-code division multiple access (TDD-CDMA) cellular networks with two-hop relay architecture to support packet data transmission. The proposed mMAC is based on joint CDMA/PRMA (packet reservation multiple access) protocol and it includes BCH code selection, power control and multihop relaying. Simulation results reveal that cellular networks with two-hop relay architecture with the proposed mMAC scheme can substantially provide a good performance as well as larger cell coverage as compared to conventional TDD-CDMA single-hop cellular networks.

In this paper, we propose a multihop dynamic channel assignment (MDCA) scheme for time division multiple access (TDMA)-based multihop cellular networks. The proposed MDCA assigns channels to calls based on interference information in surrounding cells, provided by the interference information table (IIT) in the network. Two different channel searching strategies, Sequential channel searching (SCS) and packing-based channel searching (PCS), for use in MDCA are proposed and studied. A channel reassignment procedure to further enhance the performance is also investigated. Simulation results show that MDCA significantly improves the system capacity. Furthermore, the MDCA can efficiently alleviate the call blocking in hot-spot cells.

Abstract Whereas cellular systems have traditionally adopted single‐hop transmissions between the mobile station (MS) and the base station (BS), researchers expect multihop transmission to be utilised in the future. Cellular systems present several challenges, such as channel assignment, which are exacerbated by multimedia service traffic and an increasing number of subscribers. Recently, a clustered multihop cellular network (cMCN) architecture that complements traditional cellular systems has been proposed and studied using fixed channel assignment (FCA). However, the performance of FCA is limited when the traffic pattern varies in a service area. This paper proposes the use of dedicated information ports (DIPs) as clusterheads in cMCN, followed by a multihop dynamic channel assignment (mDCA) scheme. The mDCA works by assigning channels based on information about interference in surrounding cells. Two different channel searching strategies are developed and evaluated. Through computer simulation, we show that the proposed mDCA is able to achieve a significantly improved capacity which is maintained when the number of system channels is increased. Finally, issues regarding the implementation of the mDCA and future work on this topic are discussed. Copyright © 2007 John Wiley & Sons, Ltd.

Multihop cellular network (MCN) has been proposed to incorporate the flexibility of ad hoc networks into traditional cellular networks. Little work has been reported on the channel assignment for MCN. In this paper, we investigate the feasibility of applying the characteristics of the hierarchical overlaid microcell/macrocell architecture and the clustering of MANETs for channel assignment in TDMA-based MCN. Based on our deliberated clustered MCN (cMCN) structure with virtual microcells, we propose a fixed channel assignment (FCA) scheme for uplink transmission. An exact Markov chain model is developed to analyze its performance. In addition, an approximated model, which can reduce the computational load greatly, with much fewer states is also found. The analytical models are validated through computer simulation. From the results, our proposed FCA scheme can reduce the call blocking probability significantly as compared with the FCA for conventional single-hop cellular system. The salient contribution is that our proposed FCA scheme can increase the spectrum efficiency and the system capacity without increasing infrastructure cost by introducing the structure of cMCN with virtual microcells. We also provide some recommendations for future work on this topic.