Designing a Low-Latency Data Transmission System for Mobile Wireless Sensor Controls Using Optimized Communication Protocols

Energy-efficient cooperative transmission for intelligent transportation systems

The dismal performance of the traditional traffic congestion measures and motorist attitudes on Nigeria roads have not only made traffic flow unpredictable and worsening but also worrisome and defiling every measures put-in-place by the Government. To solve these problems, the study assessed traffic congestion and Intelligent Transport System (ITS) in Ibadan, Nigeria. In achieving this, the study examined among others, factors aiding traffic congestion; effectiveness of the existing traffic congestion measures; and the performance and disposition to the use of ITS in the study area. Systematic random sampling was used to administer 386 copies of a questionnaire on commercial motorists, while data collected were analyzed using both descriptive and inferential (Pearson Chi-Square and Fisher's Exact Tests) statistics. Findings revealed that faulty intersections (3.586) and excesses of traffic officers (3.558) were worsening factors aiding traffic congestion in Ibadan city. Meanwhile, electronic traffic signalization (3.558) and law enforcement and regulation (3.499) were rated the most effective existing traffic congestion measures. In other words, real-time traffic signal devices (3.609); automated travel advisory system (3.595) and road congestion pricing devices (3.581) were highly rated and favourably disposed to use ITS. However, the Chi-Square Tests shows that the use Intelligent Transport System is statistically influenced by users' level of education (Sig. value < 0.05). The study concludes that there is a total necessity and willingness to use ITS in the study area. Hence, the study recommends exigent and holistic approach in the provision and enforcement of ITS towards mitigating congestion and other suggestions for improved traffic flow in Nigerian cities including Ibadan.

Intelligent sensors with embedded AI are key to modern cyber-physical systems. They find applications in industrial automation, medical diagnostics and healthcare, smart cities, and autonomous systems. Despite their significant potential, they face several hardware challenges related to computing power, energy consumption, communication capabilities, and security, which limit their effectiveness. This article analyzes factors influencing the production and deployment of AI sensors. The key limitations are energy efficiency, computing power, scalability, and integration of AI sensors in real-time conditions. Among the main problems are the high requirements for data processing, the limitations of traditional microprocessors, and the balance between performance and energy consumption. To meet these challenges, the article presents several practical and innovative approaches, including the development of specialized microprocessors and optimized architectures for “edge computing,” which promise radical reductions in latency and power consumption. Through a synthesis of current research and practical examples, the article emphasizes the need for intermediate hardware–software solutions and standardization for mass deployment of AI sensors.

Half of the world’s population resides in cities and this percentage is going to rise in the coming years. This rapid transformation of the economy and society is making traditional city management redundant. Intensive urbanization has brought with it some challenges. Scanty resources, population growth, increased carbon footprint, reduced air quality, congestion on roads, depriving health status, and falling food quality, all are prominent parts of cities. Does this bring a question in mind is accelerated growth sustainable? The answer and solution to this is an adaption of the Smart City Concept. A smart city integrates all the available technology with the requirements of city development and builds infrastructure which can improve the quality of life of citizens. Smart cities are designed in a manner to use minimum resources with maximum efficiency in all areas of urban life. It provides smart solutions using smart technologies. Urban needs and technological advancements meet in smart cities. It manages cities more quickly and effectively by using AI, IT technologies, and robotic process automation. Intelligent transportation, intelligent energy, intelligent governance, intelligent security, intelligent healthcare are all central to the idea of smart cities. The emphasis of this essay is on smart transit as a component of smart cities. The movement of people and goods across several geographical boundaries has been significantly aided by the transportation sector. Globally, the majority of smart cities face logistical and transportation issues. Both the fast growing human population and the increase in the number of cars on the road are to blame for this. In order to design and run a sustainable transportation system, technology may be of great assistance. Robotic systems for real-time vehicle data collection, traffic management, and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with traffic congestion. The transportation industry, which faces difficulties including traffic congestion, unanticipated delays, and routing issues that cause businesses to lose money, has successfully used robotic process automation. Robotic process automation provides reliable results and affordable solutions while reducing uncertainty in the decision-making process. It is very helpful in designing and running a successful transit system. Robotic Process Automation (RPA) systems for obtaining real-time data from vehicles, buses, and trucks for traffic management and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with poor management and congestion. By examining different uses of robotic process automation, it is possible to evaluate precise prediction and detection models that are intended to forecast traffic volume, an appropriate route, traffic conditions, accurate information about timing, incidents, and which time and roadway for transport are suitable for running and mobility. The application of RPA in smart cities could be seen in Intelligent Transportation Systems’ Traffic Management, Public Transportation, Safety Management, Manufacturing, and Logistics. The research focuses on areas of concern for smart cities in the sphere of the transportation sector and related difficulties that may be solved via robotic process automation. The article emphasizes the challenges and solutions for making smart transportation more effective by using Intelligent Robotic Transportation Systems.

Rapid and tremendous advances in wireless technology, miniaturization, and Internet of things (IoT) technology have brought significant development to vehicular ad hoc networks (VANETs). VANETs and IoT together play a vital role in the current intelligent transport system (ITS). However, a VANET is highly vulnerable to various security attacks due to its highly dynamic, decentralized, open-access medium, and protocol-design-related concerns. Regarding security concerns, a black hole attack (BHA) is one such threat in which the control or data packets are dropped by the malicious vehicle, converting a safe path/link into a compromised one. Dropping data packets has a severe impact on a VANET’s performance and security and may cause road fatalities, accidents, and traffic jams. In this study, a novel solution called detection and prevention of a BHA (DPBHA) is proposed to secure and improve the overall security and performance of the VANETs by detecting BHA at an early stage of the route discovery process. The proposed solution is based on calculating a dynamic threshold value and generating a forged route request (RREQ) packet. The solution is implemented and evaluated in the NS-2 simulator and its performance and efficacy are compared with the benchmark schemes. The results showed that the proposed DPBHA outperformed the benchmark schemes in terms of increasing the packet delivery ratio (PDR) by 3.0%, increasing throughput by 6.15%, reducing the routing overhead by 3.69%, decreasing the end-to-end delay by 6.13%, and achieving a maximum detection rate of 94.66%.

Traffic prediction is a critical element of Intelligent Transportation Systems (ITS), enhancing road safety, traffic flow, and environmental sustainability. By leveraging big data and machine learning, ITS optimizes fuel consumption, route planning, and traffic signal control. This study evaluates the performance of four machine learning models—K-Nearest Neighbors (KNN), Naïve Bayes, Decision Tree, and Random Forest—for classifying traffic conditions based on 11 months of vehicle count data collected every 15 minutes. The traffic levels are categorized into four classes: Heavy Traffic, Medium Traffic, No Traffic, and Traffic Jam. Among the models, Random Forest achieved the highest overall accuracy (99%) and delivered strong precision and recall across all traffic levels, especially in distinguishing between complex traffic patterns. KNN also showed high accuracy but required more computational resources. Decision Tree performed comparably, particularly for Heavy Traffic, while Naïve Bayes struggled with lower precision and recall due to its limitations in handling complex data distributions. The results underscore the robustness of Random Forest for real-time traffic condition classification and support its integration into ITS for effective, data-driven traffic and congestion management in urban environments.

Sensor networks have become a background technology for a wide range of applications in industrial automation, smart cities, intelligent transportation systems, healthcare monitoring, environmental sensing, precision agriculture, and supervision of critical infrastructure [...].

Wireless Multimedia Sensor Networks (WMSNs) involving camera and Scalar Sensor (SS) nodes provide precise information of events occurring in the monitored region by transmitting video packets. In WMSNs, it is necessary to provide coverage of events occurring in the monitored region for longer durations of time. The Camera Sensor (CS) nodes provide the coverage of an event and transmit the video data to the Base Station (BS), when these nodes are actuated by the associated SS nodes on occurring of an event. Therefore, in the existing pieces of work, distributed actuation focuses on the coverage of an event and prolongation of the lifetime of the CS nodes. However, for distributed actuation of the CS nodes, the SS nodes play a vital role. When the data sent by the associated SS nodes in an event area exceed the preconfigured threshold, the CS nodes start sensing the event and send the video data to the BS. Therefore, in addition to the lifetime of the CS nodes, the lifetime of the SS nodes and their data reporting latencies are important aspects for distributed actuation of the CS nodes, while sending both the video and scalar data to the BS. In this work, we propose a topology management-based distributed camera actuation scheme, named TADA, to prolong the lifetime of SS nodes, and decrease the data reporting latency in event area only. The increased lifetime of the SS nodes, in turn, increases the event coverage and packet delivery ratio. To increase the lifetime of the SS nodes in an event area, the SS nodes with the most residual energies are selected as the packet aggregators. In addition, the transmission range of these nodes is decreased, and in-network packet aggregation is performed, while reporting the happening of an event to the associated CS nodes. The aggregator selection mechanism helps in balancing energy consumption of the SS nodes. Similarly, the decrease in transmission range and aggregation mechanism help in decreasing energy consumption of these nodes. The transmission range of the SS nodes is decreased using social network analysis and Coalition Formation Game (CFG). CFG also helps in decreasing the data reporting latency of an event by the SS nodes to their associated CS nodes. Performance evaluation results show that the proposed scheme, TADA, which is based on the distributed topology management protocol named T-Must, achieves high performance in terms of the lifetime of the SS nodes, data reporting latency, coverage ratio of the event, event reporting credibility index, and packet delivery ratio in an environment affected by shadow fading.

Ad hoc networks that do not depend on fixed infrastructure but instead use multi-hop wireless links to communicate are called WANETs. These networks are decentralized and self-configuring. Because of their adaptability, they play a crucial role in emergency response, defense operations, and disaster management. Nevertheless, WANET routing is extremely difficult because of the unpredictable link quality, limited energy resources, dynamic topology, and mobile nodes. Although the Ad hoc On-Demand Distance Vector (AODV) protocol's on-demand route discovery is extensively used, it has a few drawbacks, such as a high energy consumption rate, a high rate of route failures, and limited support for Quality of Service (QoS). By facilitating data exchange between the network, media access control (MAC), and physical levels, this study improves AODV. The three enhanced versions of AODV that are suggested here are ER-AODV, E-AODV, and R-AODV. These versions aim to optimize routing decisions by considering energy metrics, signal strength, and transmission circumstances. The cross-layer variations outperform typical AODV in terms of throughput, latency reduction, packet delivery ratio, and energy consumption, according to NS-2 simulation results. In wireless contexts that are both dynamic and energy-constrained, the results show that cross-layer awareness greatly improves routing efficiency, reliability, and network longevity.

ABSTRACTThe IoT network and the software‐based controller comprise the Software‐Defined Network‐based IoT (SDN‐IoT). SDN‐IoT is widely utilized in traffic control and management, smart buildings and homes, safety applications, health care monitoring and automation, and several application domains. The most challenging aspects of the SDN‐IoT network scenario are efficient routing and security issues. Hence, a novel framework of SDN‐IoT with deep learning‐based attack detection and a QoS‐aware secure routing protocol is proposed. The attack detection is employed initially for the user request using the Deep Belief Network (DBN). The detected malicious request is dropped, and the routing is developed for the normal data packet. Here, African Aquila Optimization is introduced for efficient routing based on secure QoS factors. The analysis of the proposed method based on the assessment measures such as end‐to‐end delay, energy consumption, network lifetime, packet delivery ratio, and throughput acquired the values of 1.86, 5.22, 1632.84, 0.9959, and 0.96, respectively.

Mobility and low energy consumption are considered the main requirements for wireless body area sensor networks (WBASN) used in healthcare monitoring systems (HMS). In HMS, battery-powered sensor nodes with limited energy are used to obtain vital statistics about the body. Hence, energy-efficient schemes are desired to maintain long-term and steady connectivity of the sensor nodes. A sheer amount of energy is consumed in activities such as idle listening, excessive transmission and reception of control messages, packet collisions and retransmission of packets, and poor path selection, that may lead to more energy consumption. A combination of adaptive scheduling with an energy-efficient protocol can help select an appropriate path at a suitable time to minimize the control overhead, energy consumption, packet collision, and excessive idle listening. This paper proposes a region-based energy-efficient multipath routing (REMR) approach that divides the entire sensor network into clusters with preferably multiple candidates to represent each cluster. The cluster representatives (CRs) route packets through various clusters. For routing, the energy requirement of each route is considered, and the path with minimum energy requirements is selected. Similarly, end-to-end delay, higher throughput, and packet-delivery ratio are considered for packet routing.

Peer-to-peer trust management in intelligent transportation system: An Aumann’s agreement theorem based approach

The Wireless Body Area Networks (WBAN) is an emerging type of real-time monitoring used in the healthcare sector. It has the potential to revolutionize the healthcare sector by allowing patients not to physically visit doctor clinics or hospitals for medical checkups. But implementing such a system is challenged by many factors. The most important one is energy consumption. Energy consumption of all nodes in general and that of the gateway node in particular is critical. The gateway node is responsible for conveying sensor information to medical servers or doctors. Failure of the gateway node means failure of the overall WBAN system of a patient. Thus, extending the life time of the gateway node is a desirable objective in designing WBAN systems. As energy consumption is directly related to the end-to-end delay, Packet Delivery Ratio (PDR) and medium accessibility rate. So, placement on the human body in a location that is affected the least by delay and has high PDR and medium accessibility should be chosen as the placement location for the gateway node. In this paper, we identify the optimal placement location on the human body for a gateway node in terms of end-to-end delay, PDR, and back-off duration period.

Intelligent Transportation System (ITS) is the emerging transportation which comprises of the advanced technologies such as wireless communications, video vehicle detection, distributed system architectures, human machine interface, sensing and actuating, to improve safety of the passengers, traffic congestion, fuel consumption and optimize other services of the transportation system such as real time traffic situation, electronic toll collection, automatic road enforcement and hot lanes. The current ITS systems lacks self-adaptive capability to take spontaneous decisions. The environment of the ITS system is very complex so optimal decisions cannot be taken but a good enough decision can be taken which fulfills the requirement of the system. To build an ITS system having the capability to adapt itself, this paper presents the self-adaptive requirements for the ITS. To elicit the requirements for ITS, we use scenarios based model and goal based model using the requirements Engineering processes (Elicitation, Elaboration, Specification, Validation) with MAPE-K. We use Knowledge Acquisition on Automated Specification (KAOS) approach to build goal model using Objectiver tool, and use RELAX syntax to elicit adaptive requirements for ITS.

This paper presents a resilient and energy-efficient framework for jamming mitigation in cluster-based wireless sensor networks (WSNs), addressing a critical vulnerability in hostile or interference-prone environments. The proposed approa ch integrates dynamic cluster reorganization, adaptive MAC-layer behavior, and multipath routing strategies to restore communication capabilities and sustain network functionality under jamming conditions. The framework is evaluated across heterogeneous topologies using Zigbee and Bluetooth Low Energy (BLE); both stacks were validated in a physical testbed with matched jammer and traffic conditions, while simulation was used solely to tune parameters and support sensitivity analyses. Results demonstrate significant improvements in Packet Delivery Ratio, end-to-end delay, energy consumption, and retransmission rate, with BLE showing particularly high resilience when combined with the mitigation mechanism. Furthermore, a comparative analysis of routing protocols including AODV, GAF, and LEACH reveals that hierarchical protocols achieve superior performance when integrated with the proposed method. This framework has broader applicability in mission-critical IoT domains, including environmental monitoring, industrial automation, and healthcare systems. The findings confirm that the framework offers a scalable and protocol-agnostic defense mechanism, with potential applicability in mission-critical and interference-sensitive IoT deployments.