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Abstract Despite seemingly inexorable imminent risks of food insecurity that hang over the world, especially in developing countries like Pakistan where traditional agricultural methods are being followed, there still are opportunities created by technology that can help us steer clear of food crisis threats in upcoming years. At present, the agricultural sector worldwide is rapidly pacing towards technology-driven Precision Agriculture (PA) approaches for enhancing crop protection and boosting productivity. Literature highlights the limitations of traditional approaches such as chances of human error in recognizing and counting pests, and require trained labor. Against such a backdrop, this paper proposes a smart IoT-based pest detection platform for integrated pest management, and monitoring crop field conditions that are of crucial help to farmers in real field environments. The proposed system comprises a physical prototype of a smart insect trap equipped with embedded computing to detect and classify pests. To this aim, a dataset was created featuring images of oriental fruit flies captured under varying illumination conditions in guava orchards. The size of the dataset is 1000+ images categorized into two groups: (1) fruit fly and (2) not fruit fly and a convolutional neural network (CNN) classifier was trained based on the following features: (1) Haralick features (2) Histogram of oriented gradients (3) Hu moments and (4) Color histogram. The system achieved a recall value of 86.2% for real test images with Mean Average Precision (mAP) of 97.3%. Additionally, the proposed model has been compared with numerous machine learning (ML) and deep learning (DL) based models to verify the efficacy of the proposed model. The comparative results indicated that the best performance was achieved by the proposed model with the highest accuracy, precision, recall, F1-score, specificity, and FNR with values of 97.5%, 92.82%, 98.92%, 95.00%, 95.90%, and 5.88% respectively.

In this article, a compact 4-port UWB (Ultra-Wide Band) MIMO (Multiple Input Multiple Output) antenna is proposed. A low profile FR-4 substrate is used as a dielectric material with the dimensions of 58 × 58 mm2 (0.52λ × 0.52λ) at 2.8 GHz and a standard thickness of 1.6 mm. The proposed design characterizes an impedance bandwidth starting from 2.8 to 12.1 GHz (124.1%). Each of the four elements of the proposed MIMO antenna configuration consists of a monopole antenna with PG (partial ground) that has a slot at its center. The corner of each patch (radiator) and ground slot are rounded for impedance matching. Each unit cell is in an orthogonal orientation, forming a quad-port MIMO antenna system. For reference, the partial ground of each unit cell is connected meticulously with the others. The simulated results of the proposed quad-port MIMO antenna design were configured and validated by fabrication and testing. The proposed Quad-port MIMO design has a 6.57 dBi peak gain and 97% radiation efficiency. The proposed design has good isolation below 15 dB in the lower frequency range and below 20 dB in the higher frequency range. The design has a measured ECC (Envelop Correlation Co-efficient) of 0.03 and DG (Diversity Gain) of 10 dB. The value of TARC (Total Active Reflection Coefficient) over the entire operating band is less than 10 dB. Moreover, the design maintained CCL (Channel Capacity Loss) < 0.4 bits/sec/Hz and MEG (Mean Effective Gain) < 3 dB. Based on the obtained results, the proposed design is suitable for the intended high data rate UWB wireless communication portable devices.

A self-decoupled technique is described that enables the radiating elements in the antenna array to be densely packed for multiple-input multiple-output (MIMO) wireless communications systems. High isolation between the adjacent antenna elements is obtained by fixing the radiating elements in an orthogonal configuration with respects to each other. Current from the adjacent ports cancels their impact which results in low mutual coupling. The additional benefit of this configuration is realizing a densely packed array. The ground plane of each radiating element on the array board itself are isolated to mitigate surface wave propagations to suppress mutual coupling between the antenna elements. The radiating elements are based on a modified edge-fed circular patch antenna that includes a curved slot line and open-circuited stub to widen the array’s impedance bandwidth with no impact on the antenna’s footprint size. The proposed technique was verified with the design of an antenna array of matrix size 4 × 4 centered at 3.5 GHz. The array had a measured impedance bandwidth of 4 GHz from 1.5 GHz to 5.5 GHz, which corresponds to a fractional bandwidth of 114%, peak gain of 3 dBi and radiation efficiency of 84%. Its average diversity gain and envelope correlation coefficient (ECC) over its operating band are 9.6 dB and

One of the major challenges in the area of smart grids is the management of power between consumers and generators. Traditionally, the power mismatch is managed in a centralized fashion which has major shortcomings of complexity, requires large bandwidth, ineffectiveness, and unscalable. To address these problems, this paper presents a novel distributed mismatch technique in smart grids. In this algorithm, every generation and consumer unit, has to estimate the total power that has been generated, the total load and the power mismatches. The coordination and control of power nodes is achieved through distributed manner. The proposed technique achieves through consensus algorithms. Such distributed technique prevails task sharing, surviving on single link failure, efficient decision making, the fastest convergence, and autonomy for the global power nodes. The technique is suitable for all types of grid in islanded and connected mode. We evaluated optimization factors: rapid convergence, fast computation, scalability and effectiveness. The proposed distributed network examined power systems using random, unreliable, unpredicted, and arbitrary topologies. It explores distributed node convergence, optimality, and status sharing through Graphs and Matrix theories. The communication reliability, link stability, privileges distribution, comparative cost, and adoptability of propose distributed technique has been assessed. Moreover, the proposed scheme is evaluated under different communication topologies and experimental testbed results to explain the effectiveness of the algorithm.

In this research, scalable framework for Smart Logistics based Cyber-Physical System (SLCPS) is emulated for stable coverage and connectivity of Internet of Things (IoT) devices. This work is modern manifestation of three laws of computing. Moore's and Koomey's laws recommend performance gain and energy efficiency whereas Metcalfe's law imply network scalability. Combination of these laws suggests the research proposition that development of scalable and performance efficient IoT networks is inevitable. Although IoT has improved specific logistics modules considerably, but incorporation of IoT in complete supply chain of food and random placement of IoT devices due to which unstable coverage and connectivity occurred are major challenges in logistics. The proposed SLCPS framework is designed firstly, to develop apt IoT protocol stack for logistics. Secondly, for bonded connectivity and coverage, mathematical models are proposed instead of random placement and coverage map is based on binary coverage model. Thirdly, for scalability supply chain of food for smart logistics process is designed in terms of container, storehouse and warehouse comprising of varying number of IoT devices. The architecture of SLCPS framework has three modules i.e. internal IoT network, border router and external network, emulated in Cooja simulator. The contikimac protocol is used for efficient traffic flow and power consumption. Single hop, multiple hops and random IoT devices placement scenarios are used for results comparison and validation. The performance evaluation results, i.e. throughput, network convergence time, packet delivery ratio, average latency, power consumption and timeline investigation validated utilization of proposed framework in terms of enhanced network performance. Significance of proposed SLCPS framework results in cost minimization, reducing communication and computation overhead, resilience to IoT device failures and an interference free network connectivity and coverage. Coverage and connectivity are measure of quality of service in IoT network. Therefore, this research provided bonded coverage and connectivity in smart logistics using mathematical models. In addition, a baseline framework is provided for extended research in CPS and IoT applications.

The Internet of Things (IoT) supports human endeavors by creating smart environments. Although the IoT has enabled many human comforts and enhanced business opportunities, it has also opened the door to intruders or attackers who can exploit the technology, either through attacks or by eluding it. Hence, security and privacy are the key concerns for IoT networks. To date, numerous intrusion detection systems (IDS) have been designed for IoT networks, using various optimization techniques. However, with the increase in data dimensionality, the search space has expanded dramatically, thereby posing significant challenges to optimization methods, including particle swarm optimization (PSO). In light of these challenges, this paper proposes a method called improved dynamic sticky binary particle swarm optimization (IDSBPSO) for feature selection, introducing a dynamic search space reduction strategy and a number of dynamic parameters to enhance the searchability of sticky binary particle swarm optimization (SBPSO). Through this approach, an IDS was designed to detect malicious data traffic in IoT networks. The proposed model was evaluated using two IoT network datasets: IoTID20 and UNSW-NB15. It was observed that in most cases, IDSBPSO obtained either higher or similar accuracy even with less number of features. Moreover, IDSBPSO substantially reduced computational cost and prediction time, compared with conventional PSO-based feature selection methods.

Cultivation of olive trees for the past few years has been widely spread across Mediterranean countries, including Spain, Greece, Italy, France, and Turkey. Among these countries, Spain is listed as the largest olive producing country with almost 45% of olive oil production per year. Dedicating land of over 2.4 million hectares for the olive cultivation, Spain is among the leading distributors of olives throughout the world. Due to its high significance in the country's economy, the crop yield must be recorded. Manual collection of data over such expanded fields is humanly infeasible. Remote collection of such information can be made possible through the utilization of satellite imagery. This paper presents an automated olive tree counting method based on image processing of satellite imagery. The images are pre-processed using the unsharp masking followed by improved multi-level thresholding-based segmentation. Resulting circular blobs are detected through the circular Hough transform for identification. Validation has been performed by evaluating the proposed scheme for the dataset formed by acquiring images through the “El Sistema de Información Geográfica de Parcelas Agrícolas”viewer over the region of Spain. The proposed algorithm achieves an accuracy of 96% in detection. Computation time was recorded as 24 ms for an image size of 300 × 300 pixels. The less spectral information is used in our proposed methodology resulting in a competitive accuracy with low computational cost in comparison to the state-of-the-art technique.

Video recorders record the output of each security camera. After an incident, the video footage can be used for evidence by locating a suspect or criminal for a crime. A manual scan of the video footage requires a considerable amount of manpower and time, a luxury which cannot be afforded when tracking down a person of interest (POI). An automated system is proposed in this paper which aims at finding the desired POI through the available volume of video data quickly and accurately. It is visualized to go through all the available videos and detect the POI using facial recognition. Thereafter, it would create a video montage of all the desired frames and incorporate time and location information to produce a path map followed by the POI. The proposed system reduces the human burden, human error and reduces the time taken when searching the POI manually. Validation has been performed on various video data collected by ourselves as well. The results depict that the proposed system is able to correctly identify POI with an accuracy of 86% for video data captured in a constrained environment. Videos captured by a cell phone in an unconstrained environment result in an accuracy of around 80%. Real video tested in our university campus revealed the proposed system is capable of generating tracking information for POI effectively.

All over the world, road congestion is among the most prevalent transport challenges usually in urban environments which not only increases fuel consumption and emission of harmful gases, but also causes stress for the drivers. Intelligent Transportation System (ITS) enables a better use of the infrastructure by connecting vehicles to other vehicles as well as infrastructure and thus delivers a faster communication opportunity to ensure safe and secure driving. Machine-to-machine (M2M) communication is one of the latest information and communication technologies which offers ubiquitous connectivity among several smart devices. The use of mobile (cellular) M2M communications has emerged due to the wide range, high reliability, increased data rates, decreased costs as well as easy and short-term deployment opportunities. Since the radio spectrum is a scarce resource, M2M traffic can potentially degrade the performance of mobile networks due to the large number of devices sending small-sized data. This paper presents an efficient data multiplexing scheme by using Long-Term Evolution Advanced (LTE-Advanced) Relay Nodes, which aggregates M2M traffic to maximize radio resource utilization. Extensive system-level simulations are performed using an LTE-Advanced-based model developed in the RIVERBED modeler to evaluate the performance of the proposed data multiplexing scheme. Simulation results show that approximately 40 % more smart M2M devices used in ITS and logistics are served per LTE-Advanced cell under the present system settings.

The ever-growing Internet of Things (IoT) data traffic is one of the primary research focuses of future mobile networks. 3rd Generation Partnership Project (3GPP) standards like Long Term Evolution-Advanced (LTE-A) have been designed for broadband services. However, IoT devices are mainly based on narrowband applications. Standards like LTE-A might not provide efficient spectrum utilization when serving IoT applications. The aggregation of IoT data at an intermediate node before transmission can answer the issues of spectral efficiency. The objective of this work is to utilize the low cost 3GPP fixed, inband, layer-3 Relay Node (RN) for integrating IoT traffic into 5G network by multiplexing data packets at the RN before transmission to the Base Station (BS) in the form of large multiplexed packets. Frequency resource blocks can be shared among several devices with this method. An analytical model for this scheme, developed as an r-stage Coxian process, determines the radio resource utilization and system gain achieved. The model is validated by comparing the obtained results with simulation results.