This paper presents a framework for remote monitoring of unmanned aerial vehicles (UAVs) developed specifically for urban environments through crowd sensing. This system is built around a centralized control mechanism that manages UAV traffic to reduce the risks of airspace congestion and sudden accidents. The UAVs periodically broadcast their remote identifications (RIDs), which are received by ground observers on their mobile devices through dedicated applications. These RIDs contain multiple information, including the drone's ID, location, altitude, velocity, and a timestamp. The ground observers then forward the received RIDs to the surveillance station after appending their coordinates, ID, and a time stamp. A key component of our approach is the incorporation of signal quality metrics - path loss, shadowing, and received signal strength (RSS) - along with distance considerations in the activation process of ground observers for RID message forwarding. To address potential data congestion and incentive overuse in densely populated areas, our model uses a predictive strategy for dynamic observer activation, ensuring that the surveillance system processes an optimal number of RID reports efficiently. This comprehensive consideration of both signal integrity and spatial proximity significantly improves the detection and monitoring precision of UAVs, increasing the system's ability to effectively monitor UAV activities while conserving resources in urban environments.

Outsourcing chip production is common among semiconductor vendors to cope with the increasing demand for integrated circuits. This has resulted in several security issues in the chip supply chain, including hardware trojans, intellectual property theft, and overproduction. The concept of zero trust –never trust, always verify– presents a promising solution for ensuring the authenticity of Integrated Circuits (ICs), particularly in critical systems where adversary attacks can cause significant losses or damage. The Security Protocol and Data Model (SPDM) is a reliable protocol that uses certificates to ensure the authenticity of ICs. Based on this protocol, the presented paper proposes a chip-to-chip zero-trust security architecture that aims to verify the authenticity of any connected peripheral before its use. The contributions include an overview of the proposed architecture, the anti-clock stretching technique, and an analysis of the challenges encountered during the implementation and execution.

The increasing demand for higher data rates and improved energy efficiency (EE) in next-generation wireless networks necessitates the optimized selection of multiple-access and coordination techniques. A hybrid joint transmission (JT)-coordinated multi-point (CoMP) enabled orthogonal multiple access (OMA)/non-orthogonal multiple access (NOMA) technique, combining the spectral efficiency (SE) and capacity benefits of CoMP NOMA with the interference mitigation of CoMP OMA, offers a highly adaptable solution for future wireless networks. This paper studies the joint optimization of CoMP/non-CoMP selection, OMA/NOMA selection, power allocation, and user pairing, with the objective of maximizing the EE in the network. A Dynamic CoMP user selection with energy-efficient adaptive multiple access (DCEAMA) algorithm to solve the formulated problem is proposed. Our Monte Carlo simulations show that the DCEAMA surpasses both the pure CoMP OMA and CoMP NOMA schemes in terms of EE, with an average increase of 38% and 26% respectively. We compare our heuristic technique to an exhaustive search strategy to evaluate its efficiency. The findings indicate that our strategy produces comparable EE across various power levels with reduced computational complexity.

This article presents a novel framework for integrating low-earth orbit satellites (LEOSs) with terrestrial-wired networks to improve coverage, throughput, and transmission reliability of 6th generation (6G) Internet of things (IoT) networks. The proposed framework utilizes the synergy of non-orthogonal multiple access (NOMA), automatic repeat request (ARQ), and cooperative communications to maximize the data downloaded from a LEOS to multiple terrestrial users. More specifically, we propose a novel offline packets repair and recovery (PRR) technique to reduce the number of dropped packets, where the wired-terrestrial connection is used to enable efficient bidirectional cooperation, to improve the reliability of the received data by reducing the multiuser interference inherent to NOMA. Moreover, by exchanging the acknowledgment messages used with ARQ, efficient Chase combining (CC) is applied to improve the signal to noise ratio (SNR) of the received packets. Extensive Monte Carlo simulation experiments are used to evaluate and quantify the advantages of the proposed system. The results obtained show that the proposed system can repair a significant number of dropped packets, which reduces the packet drop rate and improves the network throughput. In several scenarios, the proposed PRR managed to repair and recover more than 90% of the dropped packets.

Outsourcing chip production is common among semiconductor vendors to cope with the increasing demand for integrated circuits. This has resulted in several security issues in the chip supply chain, including hardware trojans, intellectual property theft, and overproduction. Zero-trust presents a promising solution for ensuring the authenticity of Integrated Circuits (ICs), particularly in critical systems where adversary attacks can cause significant losses or damage. The Security Protocol and Data Model (SPDM) is a reliable protocol that uses certificates to ensure the authenticity of ICs. Based on this protocol, the presented paper proposes a chip-to-chip zero-trust security architecture that aims to verify the authenticity of any connected peripheral before its use. The contributions include an overview of the proposed architecture, implementation and formal verification of the SPDM protocol, and analysis of the challenges encountered during the implementation and execution.

Nonorthogonal multiplexing (NOM) is a novel superposition coding scheme that has been recently proposed to improve the throughput of wireless systems. However, restricting the number of multiplexed packets to two limits the throughput improvement of nonorthogonal multiplexing (NOM) to 100% in best-case scenarios. Therefore, this work presents a generalized NOM (GNOM) design with an unlimited number of multiplexed packets. In the multiplexing process, new and retransmitted packets due to arq are combined while considering the impact of channel conditions on the power assigned per packet. The proposed GNOM employs an efficient heuristic algorithm to perform the power assignment and multiplexing decisions. Moreover, the complexity can be controlled by enforcing a limit on the maximum number of multiplexed packets per transmission, making it suitable for iot nodes with diverse computational capabilities and quality of service requirements. The obtained results demonstrate the effectiveness of the proposed scheme, which offers up to 200% throughput improvement at moderate signal to noise ratios (SNRs), and up to 700% at high SNRs. Furthermore, the new scheme can reduce the transmission power consumption by up to 6 dB in the high SNR region.

Due to the increasing trend in IP traffic, the placement of Data Centers (DCs) at network nodes has become a hot research topic. A proper DCs placement translates in reduced power consumption of overall network. The paper scope is to find the best placement of “k” DCs nodes out of “N” total nodes to reduce power consumption. To solve the problem, we propose two heuristics: EoDCP, based on Estimation of Distribution Algorithm (EDA), and MaxN-MinL. An exhaustive search based ESDCP algorithm is used as a lower bound to compare the performance of EoDCP and MaxN-MinL. Moreover, electronic traffic grooming technique is employed to further reduce the total network power consumption. A 20-Node Random network and a 17-Node German network are used to perform comparison of proposed heuristics. Performance of EoDCP algorithm is far better than those of MaxN-MinL, and is similar to the optimal solution obtained via ESDCP. Finally, using electronic traffic grooming improves power savings up-to 15% in the two considered topologies.

Cloud radio access network (CRAN) is a promising approach to provide ubiquitous and on demand access to future Internet of Things (IoT) networks. The existing CRANs assume a single cloud which suffers from computational complexity and signaling latency to support massive number of IoT devices in large scale network deployments. This paper focuses on the scheduling of IoT devices in a multicloud IoT network scenario. This paper considers the downlink of an IoT network consisting of multiple clouds, each coordinates a cluster of several base stations (BSs) allowing joint signal processing. The transmit frame of each BS is composed of several resource blocks (RBs). The multiple clouds are linked to the central cloud which performs scheduling of IoT devices and synchronization of transmit frames. The work models the IoT devices to RBs assignment problem considering the intercloud and intracloud interference. The optimization problem maximizes the overall network utilization under practical network constraints. Further, this paper also proposes a low complexity heuristic algorithm to solve the constraint resource allocation problem in linear time. Complexity analysis of proposed algorithm is carried out and simulations results for a number of IoT network scenarios demonstrate that proposed solution is numerically accurate and performs close to the optimal solution.

Emerging device centric communication technologies such as device to device (D2D) communication, devices to device (Ds2D) communication and multi- homing (MH) D2D have been considered as essential part of future 5G networks as well as internet of things (IoT). The device centric communication offers enhanced cellular data rates, high spectral efficiency, reduced latency, improved fairness, better energy efficiency and extended coverage; however, the battery life of end devices is crucial to fully reap benefits of this technology. In this article we propose a new method for device centric communication in IoT system, where multiple source IoT devices can send data to multiple destination IoT devices using multiple interfaces. This method is called devices to devices (Ds2Ds) communication. A tree search algorithm is proposed to select the optimal source IoT devices, destination IoT devices and radio interfaces. The results of proposed Ds2Ds communication are benchmarked against Ds2D and MH- D2D. Extensive simulation has been carried out to compare energy efficiency per source device. The simulation results show the superiority of Ds2Ds over Ds2D in terms of energy efficiency, which, in turn implies better throughput. Ds2DS is superior to MH-D2D in terms of energy consumption per source device, a very good and promising requirement for green communication.

In this paper, we present a novel smoothness-based interpolation algorithm for the division of focal plane Polarimeters (DoFP). By calculating the divided blocks' variance that represents their local smoothness, the proposed algorithm well-balances between the traditional bilinear and bicubic interpolation algorithms. In addition, compared with the previously reported gradient-based interpolation algorithm which only indicates the image's directional change along 0°, 90°, 45° and 135°, the presented smoothness-based algorithm covers the variations along all the possible directions, leading to more accurate selection between the bilinear and bicubic algorithms. According to our extensive simulation results, the proposed implementation exhibits the lowest mean square error (MSE) for the test images among all the previously reported algorithms, including bilinear, bicubic and gradient-based interpolation algorithms.

In order to meet requirements of local services in next generation cellular networks, Device to device (D2D) communication is gaining popularity as strategy to maximize overall system throughput. This paper presents a joint resource allocation (JRA) technique with the aim to maximize system throughput while observing the limitations of power and interference. The optimization problem presented is mixed integer, non linear and NP- hard. Increasing discrete variables in the problems, enhances the computational complexity exponentially and exhaustive search becomes formidable task. The problem is solved using mesh adaptive search (MADS) algorithm. It is shown that proposed strategy is suitable for solution of such kind of combinatorial problem. Computational convergence towards solution with adequate iterations shows efficacy of the proposed strategy. System throughput maximization results of simulations show suitability of the suggested scheme in this paper compared to other techniques.

Abstract Device‐to‐device (D2D) communications are being considered a way forward to achieve higher data rate targets for futuristic wireless networks. D2D introduces interference among cellular users and D2D users. A joint resource allocation (JRA) strategy in cellular network with D2D functionality can definitely enhance overall data rate. The strategy under consideration maximizes the overall data rate of cellular network besides meeting threshold of power and interference constraints. The JRA is a class of mixed integer non‐linear constraint optimization problems and is NP hard. Because of discrete nature of variables in the problem, optimal solution performs extensive search of integer variables, and problem becomes exponentially complex with the increasing number of user pairs. In this paper, mesh adaptive direct search algorithm is applied to solve the aforementioned problem. The algorithm is suitable for complex problems of combinatorial nature to solve the JRA strategy in D2D. The proposed algorithm converges to optimal solution within acceptable computational iterations. Simulation results of system capacity and interference also demonstrate the suitability of the proposed approach viz‐a‐viz other algorithms. Copyright © 2016 John Wiley & Sons, Ltd.

Heterogeneous systems on a single chip composed of a central processing unit, graphics processing unit (GPU), and field-programmable gate array (FPGA) are expected to emerge in the near future. In this context, the system on chip can be dynamically adapted to employ different architectures for execution of data-intensive applications. Motion estimation (ME) is one such task that can be accelerated using FPGA and GPU for high-performance H.264/Advanced Video Coding encoder implementation. This paper presents an inherent parallel low-complexity rate-distortion (RD) optimized fast ME algorithm well suited for parallel implementations, eliminating various data dependencies caused by a reliance on spatial predictions. In addition, this paper provides details of the GPU and FPGA implementations of the parallel algorithm by using OpenCL and Very High Speed Integrated Circuits (VHSIC) Hardware Descriptive Language (VHDL), respectively, and presents a practical performance comparison between the two implementations. The experimental results show that the proposed scheme achieves significant speedup on GPU and FPGA, and has comparable RD performance with respect to sequential fast ME algorithm.

This paper presents a VLSI architecture for a low complexity motion estimation algorithm, referred to as Slim264, for multiview video coding extension of H.264. Algorithmic modifications are introduced to obtain a fully parallel computational structure able to meet the throughput requirements of high resolution and high frame rate videos. High parallelism is achieved by predicting small blocks, i.e. 4x4 pixel blocks, in parallel and then adding them up in order to get Sum of Absolute Differences (SADs) of large block sizes. The predictor is able to support high resolution videos i.e. 1080p. The modified algorithm shows promising PSNR results with respect to full search algorithm. The predictor is synthesized with a clock frequency of 200 MHz, occupying an area of 0.49 mm2, on 90-nm Standard Cell ASIC technology.

Recently, the parallel processing capability of the Graphics Processing Unit (GPU) has been employed for accelerating motion estimation in H.264/AVC encoder implementations. However, while implementing parallel motion estimation on GPU, the bit rate cost of the motion vector is generally ignored. This is due to the unavailability of the spatially predicted motion vectors, which leads to rate-distortion performance degradation. This paper presents a fast parallel motion estimation algorithm implemented on GPU using OpenCL to tackle this problem. The predicted motion vectors are estimated from temporally predicted motion vectors and used for evaluating the bit rate cost of the motion vectors simultaneously. The experimental results show that the proposed scheme achieves significant speedup and has comparable rate-distortion performance with respect to sequential fast motion estimation algorithm.

In the recent decades most of the big organizations have adopted maturity driven process improvement efforts (MDPI). Most of these efforts have been inspired of maturity models like the CMM (capability maturity model). The maturity of an organizationpsilas processes is measured through its maturity level. An organization availed a high maturity level is considered more trustworthy In this competitive business era making software process improvement (SPI) happen is a challenge for small organizations. The statistics provided by Software Engineering Institute (SEI) for software community striving for SPI by using CMM/CMM Iindicates that a large number of companies fail to achieve their process improvement goals. SPI efforts have mostly been prolonged, expensive, and not often delivered the effects back to the organizations in the same dimension as investigations.We wonder WHY? This research paper investigates strengths and weaknesses of maturity driven process improvement (e.g. CMM). The case studies in extant SPI literature are studied and focus group is used for data collection.The study suggests that process improvement initiatives should be tailored addressing organizational needs instead of blindly pursuing maturity models prescriptions.Furthermore, it is suggested to have an inception phase prior to an SPI initiative to decide whether a maturity driven process improvement approach should be opted or an effect driven process improvement approach.